Publications

• Smarter Contracts: Detecting Vulnerabilities in Smart Contracts with Deep Transfer Learning Sendner, Christoph; Chen, Huili; Fereidooni, Hossein; Petzi, Lukas; König, Jan; Stang, Jasper; Dmitrienko, Alexandra; Sadeghi, Ahmad-Reza; Koushanfar, Farinaz; in To appear at the Network and Distributed System Security Symposium (NDSS) (2023).
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  @article{sendner2023smarter, author = {Sendner, Christoph and Chen, Huili and Fereidooni, Hossein and Petzi, Lukas and König, Jan and Stang, Jasper and Dmitrienko, Alexandra and Sadeghi, Ahmad-Reza and Koushanfar, Farinaz}, journal = {To appear at the Network and Distributed System Security Symposium (NDSS)}, keywords = {sss-group}, title = {Smarter Contracts: Detecting Vulnerabilities in Smart Contracts with Deep Transfer Learning}, year = 2023 }
• AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot Learning for Mobile Platforms Fereidooni, Hossein; König, Jan; Rieger, Philipp; Chilese, Markus; Finke, Moritz; Goekbakan, Bora; Dmitrienko, Alexandra; Sadeghi, Ahmad-Reza; in To appear at the Network and Distributed System Security Symposium (NDSS) (2023).
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  @article{hossein2023authentisense, author = {Fereidooni, Hossein and König, Jan and Rieger, Philipp and Chilese, Markus and Finke, Moritz and Goekbakan, Bora and Dmitrienko, Alexandra and Sadeghi, Ahmad-Reza}, journal = {To appear at the Network and Distributed System Security Symposium (NDSS)}, keywords = {sss-group}, title = {AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot Learning for Mobile Platforms}, year = 2023 }

• FedCRI: Federated Mobile Cyber-Risk Intelligence Fereidooni, Hossein; Dmitrienko, Alexandra; Madlener, Felix; Rieger, Phillip; Miettinen, Markus; Sadeghi, Ahmad-Reza; in The Network and Distributed System Security Symposium (NDSS) (2022).
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  @inproceedings{fereidooni2022fedcri, author = {Fereidooni, Hossein and Dmitrienko, Alexandra and Madlener, Felix and Rieger, Phillip and Miettinen, Markus and Sadeghi, Ahmad-Reza}, booktitle = {The Network and Distributed System Security Symposium (NDSS)}, keywords = {sss-group}, title = {FedCRI: Federated Mobile Cyber-Risk Intelligence}, year = 2022 }
• SCRAPS: Scalable Collective Remote Attestation for Pub-Sub IoT Networks with Untrusted Proxy Verifier. Petzi, Lukas; Ben Yahya, Ala Eddine; Dmitrienko, Alexandra; Tsudik, Gene; Prantl, Thomas; Kounev, Samuel; in USENIX Security (2022).
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  Remote Attestation (RA) is a basic security mechanism that detects malicious presence on various types of computing components, e.g., IoT devices. In a typical IoT setting, RA involves a trusted Verifier that sends a challenge to an untrusted remote Prover, which must in turn reply with a fresh and authentic evidence of being in a trustworthy state. However,most current RA schemes assume a central Verifier, which rep-resents a single point of failure. This feature is problematic when mutually suspicious stakeholders are involved. Furthermore, scalability issues arise as the number of IoT devices (Provers) grows. Although some RA schemes allow peer Provers to act as Verifiers, they involve unrealistic (for IoT devices) requirements, such as time synchronization and synchronous communication. Moreover, they incur heavy memory, computation,and communication burdens, while not considering sleeping or otherwise disconnected devices. Motivated by the need to address these limitations, we construct Scalable Collective Remote Attestation for Pub-Sub (SCRAPS), a novel collective RA scheme. It achieves scalability by outsourcing Verifier duties to a smart contract and mitigates DoS attacks against both Provers and Verifiers. It also removes the need for synchronous communication. Furthermore,RA evidence in SCRAPS is publicly verifiable, which significantly reduces the number of attestation evidence computations, thus lowering Prover burden. We report on a prototype of SCRAPS over Hyperledger Sawtooth (a blockchain geared for IoT use-cases) and evaluate its performance, scalability, and security aspects.

  @proceedings{petzi2022scraps, abstract = {Remote Attestation (RA) is a basic security mechanism that detects malicious presence on various types of computing components, e.g., IoT devices. In a typical IoT setting, RA involves a trusted Verifier that sends a challenge to an untrusted remote Prover, which must in turn reply with a fresh and authentic evidence of being in a trustworthy state. However,most current RA schemes assume a central Verifier, which rep-resents a single point of failure. This feature is problematic when mutually suspicious stakeholders are involved. Furthermore, scalability issues arise as the number of IoT devices (Provers) grows. Although some RA schemes allow peer Provers to act as Verifiers, they involve unrealistic (for IoT devices) requirements, such as time synchronization and synchronous communication. Moreover, they incur heavy memory, computation,and communication burdens, while not considering sleeping or otherwise disconnected devices. Motivated by the need to address these limitations, we construct Scalable Collective Remote Attestation for Pub-Sub (SCRAPS), a novel collective RA scheme. It achieves scalability by outsourcing Verifier duties to a smart contract and mitigates DoS attacks against both Provers and Verifiers. It also removes the need for synchronous communication. Furthermore,RA evidence in SCRAPS is publicly verifiable, which significantly reduces the number of attestation evidence computations, thus lowering Prover burden. We report on a prototype of SCRAPS over Hyperledger Sawtooth (a blockchain geared for IoT use-cases) and evaluate its performance, scalability, and security aspects.}, author = {Petzi, Lukas and Ben Yahya, Ala Eddine and Dmitrienko, Alexandra and Tsudik, Gene and Prantl, Thomas and Kounev, Samuel}, booktitle = {USENIX Security}, keywords = {sssgroup}, title = {SCRAPS: Scalable Collective Remote Attestation for Pub-Sub IoT Networks with Untrusted Proxy Verifier.}, year = 2022 }
• An Experience Report on the Suitability of a Distributed Group Encryption Scheme for an IoT Use Case Prantl, Thomas; Engel, Simon; Bauer, Andre; Ben Yahya, Ala Eddine; Herrnleben, Stefan; Iffländer, Lukas; Dmitrienko, Alexandra; Kounev, Samuel; in IEEE 95th Vehicular Technology Conference (VTC) (2022).
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  @inproceedings{PrEnBaAlHeIfDmKo2022-IoT/CPS-DistributedGroupEncryption, author = {Prantl, Thomas and Engel, Simon and Bauer, Andre and Ben Yahya, Ala Eddine and Herrnleben, Stefan and Iffländer, Lukas and Dmitrienko, Alexandra and Kounev, Samuel}, booktitle = {IEEE 95th Vehicular Technology Conference (VTC)}, keywords = {sssgroup}, organization = {IEEE}, title = {An Experience Report on the Suitability of a Distributed Group Encryption Scheme for an IoT Use Case}, year = 2022 }
• Contact Discovery in Mobile Messengers: Low-cost Attacks, Quantitative Analyses, and Efficient Mitigations Hagen, Christoph; Weinert, Christian; Sendner, Christoph; Dmitrienko, Alexandra; Schneider, Thomas; in ACM Transactions on Privacy and Security (TOPS) (2022).
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  @article{hagen2022contact, author = {Hagen, Christoph and Weinert, Christian and Sendner, Christoph and Dmitrienko, Alexandra and Schneider, Thomas}, journal = {ACM Transactions on Privacy and Security (TOPS)}, keywords = {sssgroup}, title = {Contact Discovery in Mobile Messengers: Low-cost Attacks, Quantitative Analyses, and Efficient Mitigations}, year = 2022 }
• Ransomware Detection in Databases through Dynamic Analysis of Query Sequences Sendner, Christoph; Iffländer, Lukas; Schindler, Sebastian; Jobst, Michael; Dmitrienko, Alexandra; Kounev, Samuel; in IEEE Conference on Communications and Network Security (CNS) (2022).
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  @conference{sendner2022ransomware, author = {Sendner, Christoph and Iffländer, Lukas and Schindler, Sebastian and Jobst, Michael and Dmitrienko, Alexandra and Kounev, Samuel}, booktitle = {IEEE Conference on Communications and Network Security (CNS)}, keywords = {sss-group}, title = {Ransomware Detection in Databases through Dynamic Analysis of Query Sequences}, year = 2022 }
• A Survey on Secure Group Communication Schemes with Focus on IoT Communication Prantl, Thomas; Zeck, Timo; Bauer, André; Ten, Peter; Prantl, Dominik; Ben Yahya, Ala Eddine; Iffländer, Lukas; Dmitrienko, Alexandra; Krupitzer, Christian; Kounev, Samuel; in IEEE Access (2022).
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  @article{PrZeBaTePrYaIfDmKrKo_IEEEAccess_SGCSurvey, author = {Prantl, Thomas and Zeck, Timo and Bauer, André and Ten, Peter and Prantl, Dominik and Ben Yahya, Ala Eddine and Iffländer, Lukas and Dmitrienko, Alexandra and Krupitzer, Christian and Kounev, Samuel}, journal = {IEEE Access}, keywords = {sssgroup}, publisher = {IEEE}, title = {A Survey on Secure Group Communication Schemes with Focus on IoT Communication}, year = 2022 }
• Close the Gate: Detecting Backdoored Models in Federated Learning based on Client-Side Deep Layer Output Analysis Rieger, Phillip; Krauß, Torsten; Miettinen, Markus; Dmitrienko, Alexandra; Sadeghi, Ahmad-Reza; in ArXiv | arXiv:2210.07714 (2022).
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  @article{https://doi.org/10.48550/arxiv.2210.07714, author = {Rieger, Phillip and Krauß, Torsten and Miettinen, Markus and Dmitrienko, Alexandra and Sadeghi, Ahmad-Reza}, journal = {ArXiv | arXiv:2210.07714}, keywords = {torstenkrauss}, publisher = {arXiv}, title = {Close the Gate: Detecting Backdoored Models in Federated Learning based on Client-Side Deep Layer Output Analysis}, year = 2022 }
• Contact Discovery in Mobile Messengers: Low-cost Attacks, Quantitative Analyses, and Efficient Mitigations Hagen, Christoph; Weinert, Christian; Sendner, Christoph; Dmitrienko, Alexandra; Schneider, Thomas; in Cryptology ePrint Archive, Report 2022/875 (2022). (2022/875)
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  @article{hagen2022contact, author = {Hagen, Christoph and Weinert, Christian and Sendner, Christoph and Dmitrienko, Alexandra and Schneider, Thomas}, journal = {Cryptology ePrint Archive, Report 2022/875}, keywords = {sssgroup}, number = {2022/875}, publisher = {Cryptology ePrint}, title = {Contact Discovery in Mobile Messengers: Low-cost Attacks, Quantitative Analyses, and Efficient Mitigations}, year = 2022 }
• SCRAPS: Scalable Collective Remote Attestation for Pub-Sub IoT Networks with Untrusted Proxy Verifier Petzi, Lukas; Ben Yahya, Ala Eddine; Dmitrienko, Alexandra; Tsudik, Gene; Prantl, Thomas; Kounev, Samuel; (2022). (Vol. USENIX Security ’22)

  Coming Soon
  • [ Abstract ]
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  Remote Attestation (RA) is a basic security mechanism that detects malicious presence on various types of computing components, e.g., IoT devices. In a typical IoT setting, RA involves a trusted Verifier that sends a challenge to an untrusted remote Prover, which must in turn reply with a fresh and authentic evidence of being in a trustworthy state. However,most current RA schemes assume a central Verifier, which rep-resents a single point of failure. This feature is problematic when mutually suspicious stakeholders are involved. Furthermore, scalability issues arise as the number of IoT devices (Provers) grows. Although some RA schemes allow peer Provers to act as Verifiers, they involve unrealistic (for IoT devices) requirements, such as time synchronization and synchronous communication. Moreover, they incur heavy memory, computation,and communication burdens, while not considering sleeping or otherwise disconnected devices. Motivated by the need to address these limitations, we construct Scalable Collective Remote Attestation for Pub-Sub (SCRAPS), a novel collective RA scheme. It achieves scalability by outsourcing Verifier duties to a smart contract and mitigates DoS attacks against both Provers and Verifiers. It also removes the need for synchronous communication. Furthermore,RA evidence in SCRAPS is publicly verifiable, which significantly reduces the number of attestation evidence computations, thus lowering Prover burden. We report on a prototype of SCRAPS over Hyperledger Sawtooth (a blockchain geared for IoT use-cases) and evaluate its performance, scalability, and security aspects.

  @proceedings{petzi2022scraps, abstract = {Remote Attestation (RA) is a basic security mechanism that detects malicious presence on various types of computing components, e.g., IoT devices. In a typical IoT setting, RA involves a trusted Verifier that sends a challenge to an untrusted remote Prover, which must in turn reply with a fresh and authentic evidence of being in a trustworthy state. However,most current RA schemes assume a central Verifier, which rep-resents a single point of failure. This feature is problematic when mutually suspicious stakeholders are involved. Furthermore, scalability issues arise as the number of IoT devices (Provers) grows. Although some RA schemes allow peer Provers to act as Verifiers, they involve unrealistic (for IoT devices) requirements, such as time synchronization and synchronous communication. Moreover, they incur heavy memory, computation,and communication burdens, while not considering sleeping or otherwise disconnected devices. Motivated by the need to address these limitations, we construct Scalable Collective Remote Attestation for Pub-Sub (SCRAPS), a novel collective RA scheme. It achieves scalability by outsourcing Verifier duties to a smart contract and mitigates DoS attacks against both Provers and Verifiers. It also removes the need for synchronous communication. Furthermore,RA evidence in SCRAPS is publicly verifiable, which significantly reduces the number of attestation evidence computations, thus lowering Prover burden. We report on a prototype of SCRAPS over Hyperledger Sawtooth (a blockchain geared for IoT use-cases) and evaluate its performance, scalability, and security aspects.}, author = {Petzi, Lukas and Ben Yahya, Ala Eddine and Dmitrienko, Alexandra and Tsudik, Gene and Prantl, Thomas and Kounev, Samuel}, keywords = {sss-group}, note = {Coming Soon}, title = {SCRAPS: Scalable Collective Remote Attestation for Pub-Sub IoT Networks with Untrusted Proxy Verifier}, volume = {USENIX Security '22}, year = 2022 }
• Towards a Cryptography Benchmark: A View on Attribute Based Encryption Schemes Prantl, Thomas; Zeck, Timo; Iffländer, Lukas; Beierlieb, Lukas; Dmitrenko, Alexandra; Krupitzer, Christian; Kounev, Samuel; in 2022 5th Conference on Cloud and Internet of Things (CIoT) (2022).
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  @inproceedings{PrZeIfBeDmKrKo2022-CIoT-ABEBenchmark, author = {Prantl, Thomas and Zeck, Timo and Iffländer, Lukas and Beierlieb, Lukas and Dmitrenko, Alexandra and Krupitzer, Christian and Kounev, Samuel}, booktitle = {2022 5th Conference on Cloud and Internet of Things (CIoT)}, keywords = {sss-group}, organization = {IEEE}, title = {Towards a Cryptography Benchmark: A View on Attribute Based Encryption Schemes}, year = 2022 }
• Digital Contact Tracing Solutions: Promises, Pitfalls and Challenges Thien Duc, Nguyen; Markus, Miettinen; Alexandra, Dmitrienko; Ahmad-Reza, Sadeghi; Ivan, Visconti; in Cryptology ePrint Archive (2022).
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  @article{thienduc2022digital, author = {Thien Duc, Nguyen and Markus, Miettinen and Alexandra, Dmitrienko and Ahmad-Reza, Sadeghi and Ivan, Visconti}, journal = {Cryptology ePrint Archive}, keywords = {sssgroup}, month = {oct}, title = {Digital Contact Tracing Solutions: Promises, Pitfalls and Challenges}, year = 2022 }
• Digital Contact Tracing Solutions: Promises, Pitfalls and Challenges. Thien Duc, Nguyen; Markus, Miettinen; Alexandra, Dmitrienko; Ahmad-Reza, Sadeghi; Ivan, Visconti; in IEEE Transactions on Emerging Topics in Computing (2022).
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  @article{thienduc2022digital, author = {Thien Duc, Nguyen and Markus, Miettinen and Alexandra, Dmitrienko and Ahmad-Reza, Sadeghi and Ivan, Visconti}, journal = {IEEE Transactions on Emerging Topics in Computing}, keywords = {sssgroup}, month = {oct}, title = {Digital Contact Tracing Solutions: Promises, Pitfalls and Challenges.}, year = 2022 }
• Digital contact Tracing solutions: Promises, Pitfalls and Challenges. Thien Duc, Nguyen; Markus, Miettinen; Alexandra, Dmitrienko; Ahmad-Reza, Sadeghi; Ivan, Visconti; in ArXiv I arXiv 2202.06698v2 (October 2022) (2022).
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  @article{thienduc2022digital, author = {Thien Duc, Nguyen and Markus, Miettinen and Alexandra, Dmitrienko and Ahmad-Reza, Sadeghi and Ivan, Visconti}, journal = {ArXiv I arXiv 2202.06698v2 (October 2022)}, keywords = {sssgroup}, month = {oct}, title = {Digital contact Tracing solutions: Promises, Pitfalls and Challenges.}, year = 2022 }

• RIP StrandHogg: A Practical StrandHogg Attack Detection Method on Android Stang, Jasper; Dmitrienko, Alexandra; Roth, Sascha; in 14th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) (2021).
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  StrandHogg vulnerabilities affect Android’s multitasking system and threaten up to 90% of Android platforms, which translates to millions of affected users. Existing countermeasures require modification of the OS, have usability drawbacks, or are limited to the detection of certain attack versions. In this work, we aim to develop a generic, efficient, and usability-friendly attack detection method, which does not require OS modifications and can be employed by apps installed on any vulnerable Android platform. To achieve our goal, we analyze StrandHogg attack techniques and develop two countermeasures, one using Machine Learning and the other one using ActivityCounter – a reliable attack indicator, which we could synthetically engineer. Our first approach achieves an average F1 score of 92% across all attack variations, while ActivityCounter shows superior performance and efficiently detects all attack versions without false positives. ActivityCounter is the first solution without practical limitations, which can be easily deployed in practice and protect millions of affected users.

  @inproceedings{stang2021strandhogg, abstract = {StrandHogg vulnerabilities affect Android’s multitasking system and threaten up to 90% of Android platforms, which translates to millions of affected users. Existing countermeasures require modification of the OS, have usability drawbacks, or are limited to the detection of certain attack versions. In this work, we aim to develop a generic, efficient, and usability-friendly attack detection method, which does not require OS modifications and can be employed by apps installed on any vulnerable Android platform. To achieve our goal, we analyze StrandHogg attack techniques and develop two countermeasures, one using Machine Learning and the other one using ActivityCounter – a reliable attack indicator, which we could synthetically engineer. Our first approach achieves an average F1 score of 92% across all attack variations, while ActivityCounter shows superior performance and efficiently detects all attack versions without false positives. ActivityCounter is the first solution without practical limitations, which can be easily deployed in practice and protect millions of affected users.}, author = {Stang, Jasper and Dmitrienko, Alexandra and Roth, Sascha}, booktitle = {14th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec)}, keywords = {sss-group}, title = {RIP StrandHogg: A Practical StrandHogg Attack Detection Method on Android}, year = 2021 }
• Performance Evaluation for a Post-Quantum Public-Key Cryptosystem Prantl, Thomas; Prantl, Dominik; Beierlieb, Lukas; Iffländer, Lukas; Dmitrienko, Alexandra; Krupitzer, Christian; Kounev, Samuel; in IEEE 40th International Performance Computing and Communications Conference (IPCCC) (2021).
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  @inproceedings{PrPrBeIfDnKrKo2021-IPCCC-QuantumPublicKeyCryposystem, author = {Prantl, Thomas and Prantl, Dominik and Beierlieb, Lukas and Iffländer, Lukas and Dmitrienko, Alexandra and Krupitzer, Christian and Kounev, Samuel}, booktitle = {IEEE 40th International Performance Computing and Communications Conference (IPCCC)}, keywords = {sss-group}, organization = {IEEE}, title = {Performance Evaluation for a Post-Quantum Public-Key Cryptosystem}, year = 2021 }
• Benchmarking of Pre- and Post-Quantum Group Encryption Schemes with Focus on IoT Prantl, Thomas; Prantl, Dominik; Bauer, André; Iffländer, Lukas; Dmitrenko, Alexandra; Krupitzer, Christian; Kounev, Samuel; in IEEE 40th International Performance Computing and Communications Conference (IPCCC) (2021).
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  @inproceedings{PrPrBaIfDmKrKo2021-IPCCC-QuantumGroupEncryption, author = {Prantl, Thomas and Prantl, Dominik and Bauer, André and Iffländer, Lukas and Dmitrenko, Alexandra and Krupitzer, Christian and Kounev, Samuel}, booktitle = {IEEE 40th International Performance Computing and Communications Conference (IPCCC)}, keywords = {sss-group}, title = {Benchmarking of Pre- and Post-Quantum Group Encryption Schemes with Focus on IoT}, year = 2021 }
• Increasing Security in Satellite Networks Schilling, Klaus; Dmitrienko, Alexandra; in 72nd International Astronautical Congress (IAC) (2021).
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  @inproceedings{schilling2021increasing, author = {Schilling, Klaus and Dmitrienko, Alexandra}, booktitle = {72nd International Astronautical Congress (IAC)}, keywords = {sss-group}, title = {Increasing Security in Satellite Networks}, year = 2021 }
• Vision: Challenges & Opportunities. Annaaram, Murali; Asokan, N.; Atli, Buse Gül; Avestimeh, Salman; Brasser, Ferdinand; Cammarota, Rosario; Dmitrienko, Alexandra; Dziedzic, Adam; Given-Wilson, Thomas; Gunn, Lachlan J.; Kerschbaum, Florian; Koushanfar, Farinaz; Legay, Axel; Miettinen, Markus; Nguyen, Thien Duc; Papernot, Nicolas; Sadeghi, Ahmad-Reza; Schunter, Matthias; Shokri, Reza; Smith, Virginia; in Vision Paper of the Private AI Collaborative Research Institute (2021).
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  @inproceedings{annaaramvision, author = {Annaaram, Murali and Asokan, N. and Atli, Buse Gül and Avestimeh, Salman and Brasser, Ferdinand and Cammarota, Rosario and Dmitrienko, Alexandra and Dziedzic, Adam and Given-Wilson, Thomas and Gunn, Lachlan J. and Kerschbaum, Florian and Koushanfar, Farinaz and Legay, Axel and Miettinen, Markus and Nguyen, Thien Duc and Papernot, Nicolas and Sadeghi, Ahmad-Reza and Schunter, Matthias and Shokri, Reza and Smith, Virginia}, booktitle = {Vision Paper of the Private AI Collaborative Research Institute}, keywords = {sss-group}, title = {Vision: Challenges & Opportunities.}, year = 2021 }
• Security Analysis of UniNow App Zimmermann, Keven; Thesis; University of Würzburg. (2021, June).
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  We performed a security analysis of the UniNow application, focusing on private user data. The application was chosen for its high popularity and amount of sensitive information handled through its new contact tracing functionality. This functionality is used by many universities for mandatory contact tracing against COVID-19. Our analysis concerned the Android mobile application and all web services. After mapping the attack surface using many tools to automate the process, we tested the potential issues in order of impact. We reveal multiple security issues with low to critical severity. Most of these vulnera�bilities threatened private user data directly. In particular, we found multiple disclosed secret keys that were used to encrypt user data on the client side, among others. Fur�thermore, we show how an in-app browser as well as an open redirect vulnerability were easing phishing attacks, and how university access tokens were shared with UniNow. Our more severe findings include how sensitive data was sent automatically to Google servers through backup files, how strict SSL certificate checking was disabled deliberately for many university endpoints, how JavaScript could be injected into the email viewer, and how ap�pointment invitations could be brute forced to get sensitive information about past and future appointments. The two critical security issues concern how user accounts could be taken over by using an account identifier, and how an internal service was accessible by anyone, allowing to change university configurations and potentially make user devices sent their user’s university credentials to the attacker. We performed a responsible disclosure, which serves as a reference to UniNow, enabling them to fix the discovered issues. Our work tangibly improves the security of student data handled by the company.

  @mastersthesis{noauthororeditor, abstract = {We performed a security analysis of the UniNow application, focusing on private user data. The application was chosen for its high popularity and amount of sensitive information handled through its new contact tracing functionality. This functionality is used by many universities for mandatory contact tracing against COVID-19. Our analysis concerned the Android mobile application and all web services. After mapping the attack surface using many tools to automate the process, we tested the potential issues in order of impact. We reveal multiple security issues with low to critical severity. Most of these vulnera�bilities threatened private user data directly. In particular, we found multiple disclosed secret keys that were used to encrypt user data on the client side, among others. Fur�thermore, we show how an in-app browser as well as an open redirect vulnerability were easing phishing attacks, and how university access tokens were shared with UniNow. Our more severe findings include how sensitive data was sent automatically to Google servers through backup files, how strict SSL certificate checking was disabled deliberately for many university endpoints, how JavaScript could be injected into the email viewer, and how ap�pointment invitations could be brute forced to get sensitive information about past and future appointments. The two critical security issues concern how user accounts could be taken over by using an account identifier, and how an internal service was accessible by anyone, allowing to change university configurations and potentially make user devices sent their user’s university credentials to the attacker. We performed a responsible disclosure, which serves as a reference to UniNow, enabling them to fix the discovered issues. Our work tangibly improves the security of student data handled by the company.}, author = {Zimmermann, Keven}, keywords = {thesis_supervised_by_SSS_member}, month = {jun}, school = {University of Würzburg}, title = {Security Analysis of UniNow App}, type = {Bachelor Thesis}, year = 2021 }
• Towards a Group Encryption Scheme Benchmark: A View on Centralized Schemes with focus on IoT Prantl, Thomas; Ten, Peter; Iffländer, Lukas; Herrnleben, Stefan; Dmitrenko, Alexandra; Kounev, Samuel; Krupitzer, Christian; in ACM/SPEC International Conference on Performance Engineering (ICPE) (2021).
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  The number of devices connected to the Internet of Things (IoT) is continuously increasing to several billion nowadays. As those devices often share sensitive data, encryption of those data is an important issue. The pure volume of data and the complexity of communication patterns increases, and, accordingly, the importance of group encryption is recently gaining more importance. Still, the choice of the best-suited group encryption scheme for a specific application is complicated. Benchmarks can support this choice. However, while literature distinguishes three categories for the schemes (central, decentral, and hybrid), a one-fits-all benchmark seems challenging to achieve. In this paper, we go the first step towards a structured benchmark for group encryption schemes by presenting a benchmark for centralized group encryption schemes in an IoT scenario. To this end, our benchmark includes a description of workloads, a baseline scheme, a measurement setup, and metrics while also considering the requirements and features of centralized group encryption schemes.

  @inproceedings{PrTeIfHeDmKo2021-ICPE-GroupEncryptionCentralizedBenchmark, abstract = {The number of devices connected to the Internet of Things (IoT) is continuously increasing to several billion nowadays. As those devices often share sensitive data, encryption of those data is an important issue. The pure volume of data and the complexity of communication patterns increases, and, accordingly, the importance of group encryption is recently gaining more importance. Still, the choice of the best-suited group encryption scheme for a specific application is complicated. Benchmarks can support this choice. However, while literature distinguishes three categories for the schemes (central, decentral, and hybrid), a one-fits-all benchmark seems challenging to achieve. In this paper, we go the first step towards a structured benchmark for group encryption schemes by presenting a benchmark for centralized group encryption schemes in an IoT scenario. To this end, our benchmark includes a description of workloads, a baseline scheme, a measurement setup, and metrics while also considering the requirements and features of centralized group encryption schemes.}, author = {Prantl, Thomas and Ten, Peter and Iffländer, Lukas and Herrnleben, Stefan and Dmitrenko, Alexandra and Kounev, Samuel and Krupitzer, Christian}, booktitle = {ACM/SPEC International Conference on Performance Engineering (ICPE)}, keywords = {sss-group}, month = {April}, series = {ICPE'21}, title = {Towards a Group Encryption Scheme Benchmark: A View on Centralized Schemes with focus on IoT}, year = 2021 }
• Intrusion Detection Using Machine Learning in Databases Schindler, Sebastian; Thesis; University of Würzburg. (2021, April).
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  Ransomware is a known threat that had a severe impact on computer security in the past five years. This type of malware has caused financial losses of about $13 Billion in 2017 and 2018 combined. Ransomware makes a user’s data unavailable to them, only granting access again when they pay a ransom. Traditionally, ransomware targeted the computer’s filesystem. Database ransomware is a new variant of the same principle. Instead of targeting individual files, it logs into DBMSs remotely and destroys the data, leaving only a ransom message behind. In most cases, attackers do not create a backup copy of the data. In this case, the data cannot be restored by the attackers, even if the ransom is paid. In 2018, Jobst et al. presented DIMAQS, a MySQL plugin to mitigate these attacks by detecting malicious activity through a Petri net classifier. Our work recognizes the main drawback of this approach: The Petri net cannot be easily adapted to new attack scenarios and has to be re-engineered manually. To solve this problem, we design a machine learning classifier to replace the original one. This approach yields a model that detects all attacks in our tests. Unfortunately, the model also produces a high number of false positives when trying to detect attacks before any harmful queries are issued. Overall, our approach achieves a 85.23% f1-score. The performance impact of the revised plugin is nonexistent for OLAP workloads and stays under 15% for OLTP tasks.

  @mastersthesis{noauthororeditor, abstract = {Ransomware is a known threat that had a severe impact on computer security in the past five years. This type of malware has caused financial losses of about $13 Billion in 2017 and 2018 combined. Ransomware makes a user’s data unavailable to them, only granting access again when they pay a ransom. Traditionally, ransomware targeted the computer’s filesystem. Database ransomware is a new variant of the same principle. Instead of targeting individual files, it logs into DBMSs remotely and destroys the data, leaving only a ransom message behind. In most cases, attackers do not create a backup copy of the data. In this case, the data cannot be restored by the attackers, even if the ransom is paid. In 2018, Jobst et al. presented DIMAQS, a MySQL plugin to mitigate these attacks by detecting malicious activity through a Petri net classifier. Our work recognizes the main drawback of this approach: The Petri net cannot be easily adapted to new attack scenarios and has to be re-engineered manually. To solve this problem, we design a machine learning classifier to replace the original one. This approach yields a model that detects all attacks in our tests. Unfortunately, the model also produces a high number of false positives when trying to detect attacks before any harmful queries are issued. Overall, our approach achieves a 85.23% f1-score. The performance impact of the revised plugin is nonexistent for OLAP workloads and stays under 15% for OLTP tasks.}, author = {Schindler, Sebastian}, keywords = {thesis_supervised_by_sss_member}, month = {apr}, school = {University of Würzburg}, title = {Intrusion Detection Using Machine Learning in Databases}, type = {Master Thesis}, year = 2021 }
• ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning Lutz, Oliver; Chen, Huili; Fereidooni, Hossein; Sendner, Christoph; Dmitrienko, Alexandra; Sadeghi, Ahmad Reza; Koushanfar, Farinaz; in ArXiv | arXiv:2103.12607v1 (2021).
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  Ethereum smart contracts are automated decentralized applications on the blockchain that describe the terms of the agreement between buyers and sellers, reducing the need for trusted intermediaries and arbitration. However, the deployment of smart contracts introduces new attack vectors into the cryptocurrency systems. In particular, programming flaws in smart contracts can be and have already been exploited to gain enormous financial profits. It is thus an emerging yet crucial issue to detect vulnerabilities of different classes in contracts in an efficient manner. Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable, or train individual classifiers for each specific vulnerability, or demonstrate multi-class vulnerability detection without extensibility consideration. To overcome the scalability and generalization limitations of existing works, we propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for Ethereum smart contracts that support lightweight transfer learning on unseen security vulnerabilities, thus is extensible and generalizable. ESCORT leverages a multi-output NN architecture that consists of two parts: (i) A common feature extractor that learns the semantics of the input contract; (ii) Multiple branch structures where each branch learns a specific vulnerability type based on features obtained from the feature extractor. Experimental results show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract. When extended to new vulnerability types, ESCORT yields an average F1-score of 93%. To the best of our knowledge, ESCORT is the first framework that enables transfer learning on new vulnerability types with minimal modification of the DNN model architecture and re-training overhead.

  @article{lutz2021escort, abstract = {Ethereum smart contracts are automated decentralized applications on the blockchain that describe the terms of the agreement between buyers and sellers, reducing the need for trusted intermediaries and arbitration. However, the deployment of smart contracts introduces new attack vectors into the cryptocurrency systems. In particular, programming flaws in smart contracts can be and have already been exploited to gain enormous financial profits. It is thus an emerging yet crucial issue to detect vulnerabilities of different classes in contracts in an efficient manner. Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable, or train individual classifiers for each specific vulnerability, or demonstrate multi-class vulnerability detection without extensibility consideration. To overcome the scalability and generalization limitations of existing works, we propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for Ethereum smart contracts that support lightweight transfer learning on unseen security vulnerabilities, thus is extensible and generalizable. ESCORT leverages a multi-output NN architecture that consists of two parts: (i) A common feature extractor that learns the semantics of the input contract; (ii) Multiple branch structures where each branch learns a specific vulnerability type based on features obtained from the feature extractor. Experimental results show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract. When extended to new vulnerability types, ESCORT yields an average F1-score of 93%. To the best of our knowledge, ESCORT is the first framework that enables transfer learning on new vulnerability types with minimal modification of the DNN model architecture and re-training overhead.}, author = {Lutz, Oliver and Chen, Huili and Fereidooni, Hossein and Sendner, Christoph and Dmitrienko, Alexandra and Sadeghi, Ahmad Reza and Koushanfar, Farinaz}, journal = {ArXiv | arXiv:2103.12607v1}, keywords = {technical-reports}, month = {mar}, title = {ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning}, year = 2021 }
• All the Numbers are US: Large-scale Abuse of Contact Discovery in Mobile Messengers Hagen, Christoph; Weinert, Christian; Sendner, Christoph; Dmitrienko, Alexandra; Schneider, Thomas; in Network and Distributed System Security Symposium (NDSS) (2021).
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  Contact discovery allows users of mobile messengers to conveniently connect with people in their address book. In this work, we demonstrate that severe privacy issues exist in currently deployed contact discovery methods. Our study of three popular mobile messengers (WhatsApp, Signal, and Telegram) shows that, contrary to expectations, largescale crawling attacks are (still) possible. Using an accurate database of mobile phone number prefixes and very few resources, we have queried 10 % of US mobile phone numbers for WhatsApp and 100 % for Signal. For Telegram we find that its API exposes a wide range of sensitive information, even about numbers not registered with the service. We present interesting (crossmessenger) usage statistics, which also reveal that very few users change the default privacy settings. Regarding mitigations, we propose novel techniques to significantly limit the feasibility of our crawling attacks, especially a new incremental contact discovery scheme that strictly improves over Signal’s current approach. Furthermore, we show that currently deployed hashing-based contact discovery protocols are severely broken by comparing three methods for efficient hash reversal of mobile phone numbers. For this, we also propose a significantly improved rainbow table construction for non-uniformly distributed inputs that is of independent interest.

  @inproceedings{DBLP:conf/ndss/2021, abstract = {Contact discovery allows users of mobile messengers to conveniently connect with people in their address book. In this work, we demonstrate that severe privacy issues exist in currently deployed contact discovery methods. Our study of three popular mobile messengers (WhatsApp, Signal, and Telegram) shows that, contrary to expectations, largescale crawling attacks are (still) possible. Using an accurate database of mobile phone number prefixes and very few resources, we have queried 10 % of US mobile phone numbers for WhatsApp and 100 % for Signal. For Telegram we find that its API exposes a wide range of sensitive information, even about numbers not registered with the service. We present interesting (crossmessenger) usage statistics, which also reveal that very few users change the default privacy settings. Regarding mitigations, we propose novel techniques to significantly limit the feasibility of our crawling attacks, especially a new incremental contact discovery scheme that strictly improves over Signal’s current approach. Furthermore, we show that currently deployed hashing-based contact discovery protocols are severely broken by comparing three methods for efficient hash reversal of mobile phone numbers. For this, we also propose a significantly improved rainbow table construction for non-uniformly distributed inputs that is of independent interest.}, author = {Hagen, Christoph and Weinert, Christian and Sendner, Christoph and Dmitrienko, Alexandra and Schneider, Thomas}, booktitle = {Network and Distributed System Security Symposium (NDSS)}, keywords = {sss-group}, month = {feb}, publisher = {The Internet Society}, title = {All the Numbers are US: Large-scale Abuse of Contact Discovery in Mobile Messengers}, year = 2021 }
• Remote Attestation for IoT with Smart Verifier Petzi, Lukas; Thesis; University of Würzburg. (2021, January).
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  The Internet is evolving. The Internet of Things (IoT) is a disruptive technology, billions of small and smart devices are encompassing every aspect of our lives, which leads us to a new era of the Internet. IoT connects billions of heterogeneous embedded devices in large networks. Restricted capabilities of the devices in combination with various mutually mistrusting parties throughout the network pose new challenges to security. As IoT devices become increasingly important, so does trust in their secure and reliable operation. But before we can trust them, we need to establish the trust. In the past, remote attestation has emerged into a very powerful tool in order to establish trust between devices. Traditional remote attestation establishes a static trust between two devices. This approach suffers badly heterogeneity of IoT network and purely scales to a large number of devices. Furthermore, it is very vulnerable to Denial of Service attacks. Within this thesis, we present a new approach to Remote Attestation in IoT networks. Our design combines the core strength of Remote Attestation with the advantages blockchain technology in order to enable attestation and validation of attestation evidences in a scalable manner. At rst, we develop a remote attestation scheme on a low end device serving as an exemplary IoT device. Second, we design our smart verifier, a flexible system capable of validating and storing attestation evidence. The system is build on top of blockchain technology and solves several issues accompanied by Traditional Remote Attestation in IoT such as scalability and device heterogeneity. In the end, we build and evaluate a proof of concept implementation based on ARM TrustZone and Sawtooth Hyperledger. The proposed system architecture enables trust establishment in a scalable manner even in heterogeneous networks while preventing denial of service attacks

  @mastersthesis{chan2019remote, abstract = {The Internet is evolving. The Internet of Things (IoT) is a disruptive technology, billions of small and smart devices are encompassing every aspect of our lives, which leads us to a new era of the Internet. IoT connects billions of heterogeneous embedded devices in large networks. Restricted capabilities of the devices in combination with various mutually mistrusting parties throughout the network pose new challenges to security. As IoT devices become increasingly important, so does trust in their secure and reliable operation. But before we can trust them, we need to establish the trust. In the past, remote attestation has emerged into a very powerful tool in order to establish trust between devices. Traditional remote attestation establishes a static trust between two devices. This approach suffers badly heterogeneity of IoT network and purely scales to a large number of devices. Furthermore, it is very vulnerable to Denial of Service attacks. Within this thesis, we present a new approach to Remote Attestation in IoT networks. Our design combines the core strength of Remote Attestation with the advantages blockchain technology in order to enable attestation and validation of attestation evidences in a scalable manner. At rst, we develop a remote attestation scheme on a low end device serving as an exemplary IoT device. Second, we design our smart verifier, a flexible system capable of validating and storing attestation evidence. The system is build on top of blockchain technology and solves several issues accompanied by Traditional Remote Attestation in IoT such as scalability and device heterogeneity. In the end, we build and evaluate a proof of concept implementation based on ARM TrustZone and Sawtooth Hyperledger. The proposed system architecture enables trust establishment in a scalable manner even in heterogeneous networks while preventing denial of service attacks}, author = {Petzi, Lukas}, booktitle = {CVPR Workshops}, keywords = {thesis_supervised_by_sss_member}, month = {jan}, school = {University of Würzburg}, title = {Remote Attestation for IoT with Smart Verifier}, type = {Bachelor Thesis}, year = 2021 }

• Aggregatable Remote Attestation for IoT Alistarov, Vasil; Thesis; University of Würzburg. (2020, December).
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  With the recent spike of Internet of Things (IoT) and "smart" devices, there has also been an increase in the amount of attacks on IoT networks. Wide-reaching attacks such as the one from the Mirai botnet in 2016 show how crucial it is to know that a device can be trusted before initiating communication. Remote Attestation (RA) is a proven method for asserting that a device is in a benign state. It is a challenge-response process between two parties, where the first checks the trustworthiness of the second. However, it is characterizable with low scalability a critical issue in the IoT sector. In our work, we model a new RA protocol, called Aggregatable Remote Attestation, which would allow a device to process multiple RA challenges simultaneously. We base it on the already existing SIMPLE architecture and implement it as a Proof-of-Concept (PoC) by modifying the code of the Security MicroVisor the core component of SIMPLE. We evaluate our work in terms of security and performance and show that it greatly outperforms the underlying SIMPLE. We discuss the relevance of our design in relation to the IoT sphere and denote a small set of potential topics for future work and research.

  @mastersthesis{alistarov2020aggregatable, abstract = {With the recent spike of Internet of Things (IoT) and "smart" devices, there has also been an increase in the amount of attacks on IoT networks. Wide-reaching attacks such as the one from the Mirai botnet in 2016 show how crucial it is to know that a device can be trusted before initiating communication. Remote Attestation (RA) is a proven method for asserting that a device is in a benign state. It is a challenge-response process between two parties, where the first checks the trustworthiness of the second. However, it is characterizable with low scalability a critical issue in the IoT sector. In our work, we model a new RA protocol, called Aggregatable Remote Attestation, which would allow a device to process multiple RA challenges simultaneously. We base it on the already existing SIMPLE architecture and implement it as a Proof-of-Concept (PoC) by modifying the code of the Security MicroVisor the core component of SIMPLE. We evaluate our work in terms of security and performance and show that it greatly outperforms the underlying SIMPLE. We discuss the relevance of our design in relation to the IoT sphere and denote a small set of potential topics for future work and research.}, author = {Alistarov, Vasil}, keywords = {thesis_supervised_by_sss_member}, month = {dec}, school = {University of Würzburg}, title = {Aggregatable Remote Attestation for IoT}, type = {Bachelor Thesis}, year = 2020 }
• Understanding UI attacks on Android Jasper, Stang; (2020, December).
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  In this thesis, we closely examine an important type of attack against Android smart- phones that exploit weaknesses in the user interface. In particular, we study, analyze and implement the “Keystroke Inference #3” attack from the “Cloak and Dagger” paper by Fratantonio et al. [1, 2], which enables an attacker to steal sensitive input such as pass- words. The attack takes advantage of a vulnerability that was subsequently patched on all newer Android versions. Yet, it still affects a significant user base that utilizes older devices. In this paper, we present the end-to-end attack implementation of the “Keystroke Inference #3” concept and elaborate on in-depth details. In order to make the attack fea- sible certain technical challenges needed to be solved, therefore our developed approaches are presented as well. After the evaluation of the results, we show that the implementa- tion is applicable to a wide range of Android versions. We then present our novel defense technique OverlayShifter, which fully prevents the attack while being independent of operating system modifications. Moreover, characteristics that facilitate the detection of the attack are discussed.

  @mastersthesis{jasper2020understanding, abstract = {In this thesis, we closely examine an important type of attack against Android smart- phones that exploit weaknesses in the user interface. In particular, we study, analyze and implement the “Keystroke Inference #3” attack from the “Cloak and Dagger” paper by Fratantonio et al. [1, 2], which enables an attacker to steal sensitive input such as pass- words. The attack takes advantage of a vulnerability that was subsequently patched on all newer Android versions. Yet, it still affects a significant user base that utilizes older devices. In this paper, we present the end-to-end attack implementation of the “Keystroke Inference #3” concept and elaborate on in-depth details. In order to make the attack fea- sible certain technical challenges needed to be solved, therefore our developed approaches are presented as well. After the evaluation of the results, we show that the implementa- tion is applicable to a wide range of Android versions. We then present our novel defense technique OverlayShifter, which fully prevents the attack while being independent of operating system modifications. Moreover, characteristics that facilitate the detection of the attack are discussed.}, author = {Jasper, Stang}, keywords = {thesis_supervised_by_SSS_member}, month = {December}, title = {Understanding UI attacks on Android}, type = {Bachelor Thesis}, year = 2020 }
• Contact Tracing by Giant Data Collectors: Opening Pandora’s Box of Threats to Privacy, Sovereignty and National Security. Boutet, Antoine; Castelluccia, Claude; Cunche, Mathieu; Dmitrienko, Alexandra; Iovino, Vincenzo; Miettinen, Markus; Nguyen, Thien Duc; Roca, Vincent; Sadeghi, Ahmad-Reza; Vaudenay, Serge; Visconti, Ivan; Vuagnoux, Martin; (2020).
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  Many countries have introduced digital contact tracing apps to fight the COVID-19 pandemic. Such apps help to identify contacts between potentially infectious persons automatically and thus bear the promise of reducing the burden on manual contact tracers and increase tracing accuracy in situations in which people have difficulties identifying with whom they have been in contact. A number of different proposals for digital contact tracing systems have been made or deployed, ranging from heavily centralized to completely decentralized approaches, each with its own advantages and disadvantages in terms of tracing effectiveness and impact on user privacy. During the phase of highly dynamic evolution of these approaches, surprisingly, Google and Apple established an unprecedented friendship and agreed on a very special scheme for contact tracing, realizing this in the form of an API called GAEN that they quickly integrated into their mobile operating systems. A multitude of nationally rolled out tracing apps are now based on the GAEN approach. In this paper, we revisit such apps and the GAEN API on which they are built. In particular, we point out a number of very problematic aspects and threats that the GAEN approach creates through its security and privacy weaknesses but also through the threats that it poses on technological sovereignty and the public health system.

  @techreport{boutet2020contact, abstract = {Many countries have introduced digital contact tracing apps to fight the COVID-19 pandemic. Such apps help to identify contacts between potentially infectious persons automatically and thus bear the promise of reducing the burden on manual contact tracers and increase tracing accuracy in situations in which people have difficulties identifying with whom they have been in contact. A number of different proposals for digital contact tracing systems have been made or deployed, ranging from heavily centralized to completely decentralized approaches, each with its own advantages and disadvantages in terms of tracing effectiveness and impact on user privacy. During the phase of highly dynamic evolution of these approaches, surprisingly, Google and Apple established an unprecedented friendship and agreed on a very special scheme for contact tracing, realizing this in the form of an API called GAEN that they quickly integrated into their mobile operating systems. A multitude of nationally rolled out tracing apps are now based on the GAEN approach. In this paper, we revisit such apps and the GAEN API on which they are built. In particular, we point out a number of very problematic aspects and threats that the GAEN approach creates through its security and privacy weaknesses but also through the threats that it poses on technological sovereignty and the public health system.}, author = {Boutet, Antoine and Castelluccia, Claude and Cunche, Mathieu and Dmitrienko, Alexandra and Iovino, Vincenzo and Miettinen, Markus and Nguyen, Thien Duc and Roca, Vincent and Sadeghi, Ahmad-Reza and Vaudenay, Serge and Visconti, Ivan and Vuagnoux, Martin}, institution = {{EPFL, Switzerland ; Inria, France ; JMU W{\"u}rzburg, Germany ; University of Salerno, Italy ; base23, Geneva, Switzerland ; Technical University of Darmstadt, Germany}}, keywords = {tracing}, month = {dec}, title = {Contact Tracing by Giant Data Collectors: Opening Pandora’s Box of Threats to Privacy, Sovereignty and National Security.}, type = {University works}, year = 2020 }
• Evaluating the Performance of a State-of-the-Art Group-oriented Encryption Scheme for Dynamic Groups in an IoT Scenario Prantl, Thomas; Ten, Peter; Iffländer, Lukas; Dmitrenko, Alexandra; Kounev, Samuel; Krupitzer, Christian; in IEEE 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) (2020).

  Acceptance Rate: 27%
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  New emerging technologies, such as autonomous driving, intelligent buildings, and smart cities, are promising to revolutionize user experience and offer new services. The world has to undergo large scale deployment of billions of things — cost-efficient intelligent sensors that will be interconnected into extensive networks and will collect and supply data to intelligent algorithms — to make it happen. To date, however, it is challenging to secure such an infrastructure for many-fold reasons, such as resource constraints of things, large scale deployment, manyto-many communication patterns, and dynamically changing communication groups. All these factors rule out most of the state-of-the-art encryption and key-management techniques. Group encryption algorithms are well-suitable for many-tomany communication patterns typical for IoT networks, and many of them can deal with dynamic groups. There are, however, very few constructions that could potentially fulfill the computational and storage constraints of IoT devices while providing sufficient scalability for large networks. The promising candidates, such as construction by Nishat et al. [1], were not evaluated using IoT platforms and under constraints typical for IoT networks. In this paper, we aim to fill this gap and present the evaluation of a state-of-the-art group-oriented encryption scheme by Nishat et al. to identify its applicability to IoT systems. In detail, we provide a measurement workflow, a revised version of the approach, and describe a reproducible hardware testbed. Using this evaluation environment, we analyze the performance of the encryption scheme in a typical IoT scenario from a group member perspective. The results show that all calculation times can be assumed to be constant and are always below 2 seconds. The memory requirement for permanent parameters can also be considered to be constant and are below 8.5 kbit in each case. However, the information that has to be stored temporarily for group updates has turned out to be the bottleneck of the scheme, since their memory requirements increase linearly with the group size.

  @inproceedings{PrTeIfDmKo2020-MASCOTS-GroupEncryption, abstract = {New emerging technologies, such as autonomous driving, intelligent buildings, and smart cities, are promising to revolutionize user experience and offer new services. The world has to undergo large scale deployment of billions of things — cost-efficient intelligent sensors that will be interconnected into extensive networks and will collect and supply data to intelligent algorithms — to make it happen. To date, however, it is challenging to secure such an infrastructure for many-fold reasons, such as resource constraints of things, large scale deployment, manyto-many communication patterns, and dynamically changing communication groups. All these factors rule out most of the state-of-the-art encryption and key-management techniques. Group encryption algorithms are well-suitable for many-tomany communication patterns typical for IoT networks, and many of them can deal with dynamic groups. There are, however, very few constructions that could potentially fulfill the computational and storage constraints of IoT devices while providing sufficient scalability for large networks. The promising candidates, such as construction by Nishat et al. [1], were not evaluated using IoT platforms and under constraints typical for IoT networks. In this paper, we aim to fill this gap and present the evaluation of a state-of-the-art group-oriented encryption scheme by Nishat et al. to identify its applicability to IoT systems. In detail, we provide a measurement workflow, a revised version of the approach, and describe a reproducible hardware testbed. Using this evaluation environment, we analyze the performance of the encryption scheme in a typical IoT scenario from a group member perspective. The results show that all calculation times can be assumed to be constant and are always below 2 seconds. The memory requirement for permanent parameters can also be considered to be constant and are below 8.5 kbit in each case. However, the information that has to be stored temporarily for group updates has turned out to be the bottleneck of the scheme, since their memory requirements increase linearly with the group size.}, author = {Prantl, Thomas and Ten, Peter and Iffländer, Lukas and Dmitrenko, Alexandra and Kounev, Samuel and Krupitzer, Christian}, booktitle = {IEEE 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)}, keywords = {sss-group}, month = {November}, note = {Acceptance Rate: 27%}, series = {MASCOTS '20}, title = {Evaluating the Performance of a State-of-the-Art Group-oriented Encryption Scheme for Dynamic Groups in an IoT Scenario}, year = 2020 }
• Testbed for Security Testing of Smart Contracts Denk, Lukas; Thesis; University of Würzburg. (2020, November).
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  Ethereum, one of the most popular decentralized blockchain-based platforms [1], manages cryptocurrency worth more than 40 billion US Dollars [2]. A lot of the money is controlled by autonomous programs, so called smart contracts. They are executed on the platform and everyone can call their functions; vulnerabilities are therefore easily exploited. The immutable nature of the blockchain sharpens the problem even further by prohibiting belated bug fixes. In fact, there were several cases where attackers were able to steal cryptocurrency worth several millions of US Dollar [3]. This emphasises the need of soundly testing a smart contract before deployment. Unfortunately, existing security analysing tools each cover a different subsets of vulnerabilities [4]. Moreover, even when they test for the same security issues, they often find different results. A solid test run should therefore contain several tools, which makes the whole procedure very laborious. For these reasons, we provide a testbed which runs on a virtual machine (VM) with several smart contract security analysing tools integrated. It does not only offer a command line- but also an intuitive web interface. Both interfaces allow the user to analyse his contract with all the underlying tools in a single step. Additionally, our testbed provides a detailed report for each of the tools’ test runs as well as an overview of the overall security issues the different tools found. Our evaluation shows that our testbed identifies significantly more potential security issues than each of the compared smart contract analysing tools individually does. Indeed, we found 92% of our analysed contracts as potentially being vulnerable, while even the most sensitive tool only flagged 76% of its successfully analysed contracts. Furthermore, we observed that occasionally, two tools testing for the same vulnerability, contradict themselves in more than 81% of the contracts successfully analysed by both tools

  @mastersthesis{denk2020testbed, abstract = {Ethereum, one of the most popular decentralized blockchain-based platforms [1], manages cryptocurrency worth more than 40 billion US Dollars [2]. A lot of the money is controlled by autonomous programs, so called smart contracts. They are executed on the platform and everyone can call their functions; vulnerabilities are therefore easily exploited. The immutable nature of the blockchain sharpens the problem even further by prohibiting belated bug fixes. In fact, there were several cases where attackers were able to steal cryptocurrency worth several millions of US Dollar [3]. This emphasises the need of soundly testing a smart contract before deployment. Unfortunately, existing security analysing tools each cover a different subsets of vulnerabilities [4]. Moreover, even when they test for the same security issues, they often find different results. A solid test run should therefore contain several tools, which makes the whole procedure very laborious. For these reasons, we provide a testbed which runs on a virtual machine (VM) with several smart contract security analysing tools integrated. It does not only offer a command line- but also an intuitive web interface. Both interfaces allow the user to analyse his contract with all the underlying tools in a single step. Additionally, our testbed provides a detailed report for each of the tools’ test runs as well as an overview of the overall security issues the different tools found. Our evaluation shows that our testbed identifies significantly more potential security issues than each of the compared smart contract analysing tools individually does. Indeed, we found 92% of our analysed contracts as potentially being vulnerable, while even the most sensitive tool only flagged 76% of its successfully analysed contracts. Furthermore, we observed that occasionally, two tools testing for the same vulnerability, contradict themselves in more than 81% of the contracts successfully analysed by both tools}, author = {Denk, Lukas}, keywords = {thesis_supervised_by_SSS_member}, month = {November}, school = {University of Würzburg}, title = {Testbed for Security Testing of Smart Contracts}, type = {Bachelor Thesis}, year = 2020 }
• Detection of Software Vulnerabilities in Smart Contracts using Deep Learning Lutz, Oliver; Thesis; University of Würzburg. (2020, October).
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  Der Missbrauch von Programmierfehlern in Kryptowährungsplattformen kann zu großen wirtschaftlichen Schäden fuhren. Deshalb wird in dieser Arbeit analysiert, wie Deep-Learning genutzt werden kann, um Schwachstellen in Ethereum Smart Contracts aufzudecken. Als Ergebnis ist ein Framework entwickelt worden, das sich um die Datengewinnung, Vorklassifizierung und Modellbildung kummert. Mit diesem Framework können Deep-Learning-Modelle entwickelt, trainiert und evaluiert werden, um einen KI-Scanner zu erstellen. Selbst bei einfachen neuronalen Netz-Architekturen erreicht der KI-Scanner, der acht verschiedene Arten von Schwachstellen erkennen kann, eine Genauigkeit von 98%. Dieser KI-Scanner kann zur weiteren Verwendung eingesetzt werden. Er kann jedoch auch um Schwachstellentypen erweitert werden, um ein noch mächtigeres Tool zu bilden.

  @mastersthesis{lutz2020detection, abstract = {Der Missbrauch von Programmierfehlern in Kryptowährungsplattformen kann zu großen wirtschaftlichen Schäden fuhren. Deshalb wird in dieser Arbeit analysiert, wie Deep-Learning genutzt werden kann, um Schwachstellen in Ethereum Smart Contracts aufzudecken. Als Ergebnis ist ein Framework entwickelt worden, das sich um die Datengewinnung, Vorklassifizierung und Modellbildung kummert. Mit diesem Framework können Deep-Learning-Modelle entwickelt, trainiert und evaluiert werden, um einen KI-Scanner zu erstellen. Selbst bei einfachen neuronalen Netz-Architekturen erreicht der KI-Scanner, der acht verschiedene Arten von Schwachstellen erkennen kann, eine Genauigkeit von 98%. Dieser KI-Scanner kann zur weiteren Verwendung eingesetzt werden. Er kann jedoch auch um Schwachstellentypen erweitert werden, um ein noch mächtigeres Tool zu bilden.}, author = {Lutz, Oliver}, keywords = {thesis_supervised_by_SSS_member}, month = {October}, school = {University of Würzburg}, title = {Detection of Software Vulnerabilities in Smart Contracts using Deep Learning}, type = {Master Thesis}, year = 2020 }
• LegIoT: Ledgered Trust Management Platform for IoT Neureither, Jens; Dmitrienko, Alexandra; Koisser, David; Brasser, Ferdinand; Sadeghi, Ahmad-Reza; in European Symposium on Research in Computer Security (ESORICS) (2020).
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  We investigate and address the currently unsolved problem of trust establishment in large-scale Internet of Things (IoT) networks where heterogeneous devices and mutually mistrusting stakeholders are involved. We design, prototype and evaluate LegIoT, a novel, probabilistic trust management system that enables secure, dynamic and flexible(yet inexpensive) trust relationships in large IoT networks. The core component of LegIoT is a novel graph-based scheme that allows network devices (graph nodes) to re-use the already existing trust associations(graph edges) very efficiently; thus, significantly reducing the number of individually conducted trust assessments. Since no central trusted third party exists, LegIoT leverages Distributed Ledger Technology(DLT) to create and manage the trust relation graph in a decentralized manner.The trust assessment among devices can be instantiated by any appropriate assessment technique, for which we focus on remote attestation(integrity verification) in this paper. We prototyped LegIoT for Hyper-ledger Sawtooth and demonstrated through evaluation that the number of trust assessments in the network can be significantly reduced – e.g.,by a factor of 20 for a network of 400 nodes and factor 5 for 1000 nodes.

  @inproceedings{neureither2020legiot, abstract = {We investigate and address the currently unsolved problem of trust establishment in large-scale Internet of Things (IoT) networks where heterogeneous devices and mutually mistrusting stakeholders are involved. We design, prototype and evaluate LegIoT, a novel, probabilistic trust management system that enables secure, dynamic and flexible(yet inexpensive) trust relationships in large IoT networks. The core component of LegIoT is a novel graph-based scheme that allows network devices (graph nodes) to re-use the already existing trust associations(graph edges) very efficiently; thus, significantly reducing the number of individually conducted trust assessments. Since no central trusted third party exists, LegIoT leverages Distributed Ledger Technology(DLT) to create and manage the trust relation graph in a decentralized manner.The trust assessment among devices can be instantiated by any appropriate assessment technique, for which we focus on remote attestation(integrity verification) in this paper. We prototyped LegIoT for Hyper-ledger Sawtooth and demonstrated through evaluation that the number of trust assessments in the network can be significantly reduced – e.g.,by a factor of 20 for a network of 400 nodes and factor 5 for 1000 nodes.}, author = {Neureither, Jens and Dmitrienko, Alexandra and Koisser, David and Brasser, Ferdinand and Sadeghi, Ahmad-Reza}, booktitle = {European Symposium on Research in Computer Security (ESORICS)}, keywords = {trust-management}, month = {sep}, title = {LegIoT: Ledgered Trust Management Platform for IoT}, year = 2020 }
• Mind the GAP: Security & Privacy Risks of Contact Tracing Apps Baumgärtner, Lars; Dmitrienko, Alexandra; Freisleben, Bernd; Höchst, Jonas; Kühlberg, Joshua; Mezini, Mira; Miettinen, Markus; Muhamedagic, Anel; Nguyen, Thien Duc; Penning, Alvar; Pustelnik, Dermot Frederik; Roos, Filipp; Sadeghi, Ahmad-Reza; Schwarz, Michael; Uhl, Christian; in TrustCom, Security Track (2020).
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  Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy, the so-called "Google/Apple Proposal", which we abbreviate by "GAP". We demonstrate that in real-world scenarios the current GAP design is vulnerable to (i) profiling and possibly de-anonymizing infected persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts with the potential of affecting the accuracy of an app-based contact tracing system. For both types of attack, we have built tools that can easily be used on mobile phones or Raspberry Pis (e.g., Bluetooth sniffers). The goal of our work is to perform a reality check towards possibly providing empirical real-world evidence for these two privacy and security risks. We hope that our findings provide valuable input for developing secure and privacy-preserving digital contact tracing systems.

  @inproceedings{baumgartner2020security, abstract = {Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy, the so-called "Google/Apple Proposal", which we abbreviate by "GAP". We demonstrate that in real-world scenarios the current GAP design is vulnerable to (i) profiling and possibly de-anonymizing infected persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts with the potential of affecting the accuracy of an app-based contact tracing system. For both types of attack, we have built tools that can easily be used on mobile phones or Raspberry Pis (e.g., Bluetooth sniffers). The goal of our work is to perform a reality check towards possibly providing empirical real-world evidence for these two privacy and security risks. We hope that our findings provide valuable input for developing secure and privacy-preserving digital contact tracing systems.}, author = {Baumgärtner, Lars and Dmitrienko, Alexandra and Freisleben, Bernd and Höchst, Jonas and Kühlberg, Joshua and Mezini, Mira and Miettinen, Markus and Muhamedagic, Anel and Nguyen, Thien Duc and Penning, Alvar and Pustelnik, Dermot Frederik and Roos, Filipp and Sadeghi, Ahmad-Reza and Schwarz, Michael and Uhl, Christian}, booktitle = {TrustCom, Security Track}, keywords = {sss-group}, month = {September}, title = {Mind the GAP: Security & Privacy Risks of Contact Tracing Apps}, year = 2020 }
• All the Numbers are US: Large-scale Abuse of Contact Discovery in Mobile Messengers Hagen, Christoph; Weinert, Christian; Sendner, Christoph; Dmitrienko, Alexandra; Schneider, Thomas; in Cryptology ePrint Archive, Report 2020/1119 (2020).
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  @article{cryptoeprint:2020:1119, author = {Hagen, Christoph and Weinert, Christian and Sendner, Christoph and Dmitrienko, Alexandra and Schneider, Thomas}, journal = {Cryptology ePrint Archive, Report 2020/1119}, keywords = {technical-reports}, month = {sep}, title = {All the Numbers are US: Large-scale Abuse of Contact Discovery in Mobile Messengers}, year = 2020 }
• Evaluating the Privacy of Contact Discovery Sendner, Christoph; Thesis; University of Würzburg. (2020, July).
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  Currently deployed contact discovery of mobile messengers is based on the transmission of phone numbers to the service provider. This information is private and anonymized by hashing them. In this work, we show, that this anonymization is pseudo-anonymous and can easily be broken by an attacker. For that, we develop two hash reversal techniques: one using brute-force approach and another one using look-up databases. We provide generic architectures for each of the ap- proaches. Additionally, we provide and compare two instantiations for each. Furthermore, we evaluate and compare them to the third approach based on rainbow tables. The evaluation shows near instant lookup-times of under 0.1 ms using in-memory lookup databases, this approach is however costly in terms of memory – it would require over 10 TB RAM, which would be difficult to obtain. Our brute-force approach shows an astonishing performance, being able to reverse any mobile number in under 100 seconds using consumer-level hardware. The rainbow tables produce lookup-times of 4.5 minutes with a success rate of over 99.99%. The results of our evaluation demonstrate, that hash reversals of mobile phone numbers are practical and near instant. Thus, an attacker can easily reverse hash digests of mobile phone numbers and de-anonymize personally identifiable information – like phone numbers transmitted to the service provider of mobile messenger apps.

  @mastersthesis{sendner2020contactdiscovery, abstract = {Currently deployed contact discovery of mobile messengers is based on the transmission of phone numbers to the service provider. This information is private and anonymized by hashing them. In this work, we show, that this anonymization is pseudo-anonymous and can easily be broken by an attacker. For that, we develop two hash reversal techniques: one using brute-force approach and another one using look-up databases. We provide generic architectures for each of the ap- proaches. Additionally, we provide and compare two instantiations for each. Furthermore, we evaluate and compare them to the third approach based on rainbow tables. The evaluation shows near instant lookup-times of under 0.1 ms using in-memory lookup databases, this approach is however costly in terms of memory – it would require over 10 TB RAM, which would be difficult to obtain. Our brute-force approach shows an astonishing performance, being able to reverse any mobile number in under 100 seconds using consumer-level hardware. The rainbow tables produce lookup-times of 4.5 minutes with a success rate of over 99.99%. The results of our evaluation demonstrate, that hash reversals of mobile phone numbers are practical and near instant. Thus, an attacker can easily reverse hash digests of mobile phone numbers and de-anonymize personally identifiable information – like phone numbers transmitted to the service provider of mobile messenger apps.}, author = {Sendner, Christoph}, keywords = {thesis_supervised_by_sss_member}, month = {July}, school = {University of Würzburg}, title = {Evaluating the Privacy of Contact Discovery}, type = {Master Thesis}, year = 2020 }
• Mind the GAP: Security & Privacy Risks of Contact Tracing Apps Baumgärtner, Lars; Dmitrienko, Alexandra; Freisleben, Bernd; Gruler, Alexander; Höchst, Jonas; Kühlberg, Joshua; Mezini, Mira; Mitev, Richard; Miettinen, Markus; Muhamedagic, Anel; Nguyen, Thien Duc; Penning, Alvar; Pustelnik, Dermot Frederik; Roos, Filipp; Sadeghi, Ahmad-Reza; Schwarz, Michael; Uhl, Christian; in ArXiv | arXiv:2006.05914v2 (2020).
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  Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy, the so-called "Google/Apple Proposal", which we abbreviate by "GAP". We demonstrate that in real-world scenarios the current GAP design is vulnerable to (i) profiling and possibly de-anonymizing infected persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts with the potential of affecting the accuracy of an app-based contact tracing system. For both types of attack, we have built tools that can easily be used on mobile phones or Raspberry Pis (e.g., Bluetooth sniffers). The goal of our work is to perform a reality check towards possibly providing empirical real-world evidence for these two privacy and security risks. We hope that our findings provide valuable input for developing secure and privacy-preserving digital contact tracing systems.

  @article{mindthegap, abstract = {Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy, the so-called "Google/Apple Proposal", which we abbreviate by "GAP". We demonstrate that in real-world scenarios the current GAP design is vulnerable to (i) profiling and possibly de-anonymizing infected persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts with the potential of affecting the accuracy of an app-based contact tracing system. For both types of attack, we have built tools that can easily be used on mobile phones or Raspberry Pis (e.g., Bluetooth sniffers). The goal of our work is to perform a reality check towards possibly providing empirical real-world evidence for these two privacy and security risks. We hope that our findings provide valuable input for developing secure and privacy-preserving digital contact tracing systems.}, author = {Baumgärtner, Lars and Dmitrienko, Alexandra and Freisleben, Bernd and Gruler, Alexander and Höchst, Jonas and Kühlberg, Joshua and Mezini, Mira and Mitev, Richard and Miettinen, Markus and Muhamedagic, Anel and Nguyen, Thien Duc and Penning, Alvar and Pustelnik, Dermot Frederik and Roos, Filipp and Sadeghi, Ahmad-Reza and Schwarz, Michael and Uhl, Christian}, journal = {ArXiv | arXiv:2006.05914v2}, keywords = {technical-reports}, month = {jun}, title = {Mind the GAP: Security & Privacy Risks of Contact Tracing Apps}, year = 2020 }
• SIMPL: Secure IoT Management Platform Prantl, Thomas; Ben Yahya, Ala Eddine; Dmitrienko, Alexandra; Kounev, Samuel; Lipp, Fabian; Hock, David; Rathfelder, Christoph; Hofherr, Martin; in ITG Workshop on IT Security (ITSec) (2020).
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  The proliferation of IoT devices is increasing at a fast pace, whether for private or business use. However,despite their growing popularity, their safe operation is often not guaranteed. To tackle the security challenges of modern IoT environments, the German Federal Ministry of Education and Research is funding the SIMPL project in order to support research in this area. The contribution of this paper is to introduce the main goal, objectives, and key features of SIMPL

  @inproceedings{prantl2020simpl, abstract = {The proliferation of IoT devices is increasing at a fast pace, whether for private or business use. However,despite their growing popularity, their safe operation is often not guaranteed. To tackle the security challenges of modern IoT environments, the German Federal Ministry of Education and Research is funding the SIMPL project in order to support research in this area. The contribution of this paper is to introduce the main goal, objectives, and key features of SIMPL}, author = {Prantl, Thomas and Ben Yahya, Ala Eddine and Dmitrienko, Alexandra and Kounev, Samuel and Lipp, Fabian and Hock, David and Rathfelder, Christoph and Hofherr, Martin}, booktitle = {ITG Workshop on IT Security (ITSec)}, keywords = {sss-group}, month = {may}, title = {SIMPL: Secure IoT Management Platform}, year = 2020 }
• Strategies for the Security Assessment of IoT Devices by Certification Authorities Finke, Moritz Anton; Thesis; University of Würzburg; University of Würzburg. (2020, May).
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  The Internet of Things (IoT) is a global infrastructure that interconnects physical and virtual things based on evolving information and communication technologies. Along with the rise of the IoT, multiple unprecedented forms of security issues and attacks have emerged. While their limited resources disallow the utilization of computing-intensive security measures, the services of IoT devices represent an attractive target for vulnerability exploitation. Certification Authorities (CAs) have responded to the trend and offer security assessments and certification for IoT devices. To ensure qualitative and fair certification, security testing requires well-defined strategies. This bachelor thesis analyzes the existing IoT security assessment models and proposes a novel, CA oriented Testing Guide Model (TGM) for standardized and reproducible assessments. The TGM describes a complete security assessment approach for application in IoT device security certification procedures of CAs. As part of the bachelor thesis, the TGM is specified, implemented, and its applicability evaluated with the help of existing IoT devices. CAs account for the security of certified products. The certification of devices that (subsequently) prove to be insecure can lead to a consumer's loss of trust in the CA. Therefore, the decision for issuing certificates must be comprehensible and made carefully. For the risk estimation of security issues and vulnerabilities, it is common to make use of numerical scoring systems. Well-defined scoring systems allow precise and reproducible estimations. In addition to the introduction of the TGM, this bachelor thesis analyzes the scoring systems established in the field of IT security in regard to their applicability for the scoring of IoT related vulnerabilities, complete IoT devices, and for the specific requirements of CAs. This thesis uncovers incompatibilities and insufficiencies in the existing systems that disallow application for the scoring of IoT devices and for application by CAs. To counter these issues, the thesis introduces a novel Security Scoring System (SSS) for rating IoT devices based on the risks of their vulnerabilities, services, and operational contexts. The SSS is integrated into the TGM, implemented in software, and evaluated with a comparison to the existing scoring systems. To summarize, the contributions of this bachelor thesis are two novel models for the security assessment of devices of the IoT: the TGM and the SSS. The models are intended to find application for CAs and both standardize and improve the security testing and assessment procedures.

  @mastersthesis{finke2020strategies, abstract = {The Internet of Things (IoT) is a global infrastructure that interconnects physical and virtual things based on evolving information and communication technologies. Along with the rise of the IoT, multiple unprecedented forms of security issues and attacks have emerged. While their limited resources disallow the utilization of computing-intensive security measures, the services of IoT devices represent an attractive target for vulnerability exploitation. Certification Authorities (CAs) have responded to the trend and offer security assessments and certification for IoT devices. To ensure qualitative and fair certification, security testing requires well-defined strategies. This bachelor thesis analyzes the existing IoT security assessment models and proposes a novel, CA oriented Testing Guide Model (TGM) for standardized and reproducible assessments. The TGM describes a complete security assessment approach for application in IoT device security certification procedures of CAs. As part of the bachelor thesis, the TGM is specified, implemented, and its applicability evaluated with the help of existing IoT devices. CAs account for the security of certified products. The certification of devices that (subsequently) prove to be insecure can lead to a consumer's loss of trust in the CA. Therefore, the decision for issuing certificates must be comprehensible and made carefully. For the risk estimation of security issues and vulnerabilities, it is common to make use of numerical scoring systems. Well-defined scoring systems allow precise and reproducible estimations. In addition to the introduction of the TGM, this bachelor thesis analyzes the scoring systems established in the field of IT security in regard to their applicability for the scoring of IoT related vulnerabilities, complete IoT devices, and for the specific requirements of CAs. This thesis uncovers incompatibilities and insufficiencies in the existing systems that disallow application for the scoring of IoT devices and for application by CAs. To counter these issues, the thesis introduces a novel Security Scoring System (SSS) for rating IoT devices based on the risks of their vulnerabilities, services, and operational contexts. The SSS is integrated into the TGM, implemented in software, and evaluated with a comparison to the existing scoring systems. To summarize, the contributions of this bachelor thesis are two novel models for the security assessment of devices of the IoT: the TGM and the SSS. The models are intended to find application for CAs and both standardize and improve the security testing and assessment procedures.}, address = {University of Würzburg}, author = {Finke, Moritz Anton}, keywords = {thesis_supervised_by_SSS_member}, month = {may}, school = {University of Würzburg}, title = {Strategies for the Security Assessment of IoT Devices by Certification Authorities}, type = {Bachelor Thesis}, year = 2020 }

• Implementation and Evaluation of a Group Encryption Scheme Ten, Peter; Thesis; University of Würzburg. (2019, December).
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  @mastersthesis{2019-BA-Peter-Ten, author = {Ten, Peter}, keywords = {thesis_supervised_by_SSS_member}, month = {December}, school = {University of Würzburg}, title = {Implementation and Evaluation of a Group Encryption Scheme}, type = {Bachelor Thesis}, year = 2019 }
• DR.SGX: Automated and Adjustable Side-Channel Protection for SGX using Data Location Randomization Brasser, Ferdinand; Capkun, Srdjan; Dmitrienko, Alexandra; Frassetto, Tommaso; Kostiainen, Kari; Sadeghi, Ahmad-Reza; in Annual Computer Security Applications Conference (ACSAC) (2019).
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  Recent research has demonstrated that Intel’s SGX is vulnerable to software-based side-channel attacks. In a common attack, the adversary monitors CPU caches to infer secret-dependent data accesses patterns. Known defenses have major limitations, as they require either error-prone developer assistance, incur extremely high runtime overhead, or prevent only specific attacks.In this paper, we propose data location randomization as a novel defense against side-channel attacks that target data access patterns.Our goal is to break the link between the memory observations by the adversary and the actual data accesses by the victim. We design and implement a compiler-based tool called DR.SGX that instruments the enclave code, permuting data locations at fine granularity.To prevent correlation of repeated memory accesses we periodically re-randomize all enclave data. Our solution requires no developer assistance and strikes the balance between side-channel protection and performance based on an adjustable security parameter.

  @inproceedings{brasser2019drsgx, abstract = {Recent research has demonstrated that Intel’s SGX is vulnerable to software-based side-channel attacks. In a common attack, the adversary monitors CPU caches to infer secret-dependent data accesses patterns. Known defenses have major limitations, as they require either error-prone developer assistance, incur extremely high runtime overhead, or prevent only specific attacks.In this paper, we propose data location randomization as a novel defense against side-channel attacks that target data access patterns.Our goal is to break the link between the memory observations by the adversary and the actual data accesses by the victim. We design and implement a compiler-based tool called DR.SGX that instruments the enclave code, permuting data locations at fine granularity.To prevent correlation of repeated memory accesses we periodically re-randomize all enclave data. Our solution requires no developer assistance and strikes the balance between side-channel protection and performance based on an adjustable security parameter.}, author = {Brasser, Ferdinand and Capkun, Srdjan and Dmitrienko, Alexandra and Frassetto, Tommaso and Kostiainen, Kari and Sadeghi, Ahmad-Reza}, booktitle = {Annual Computer Security Applications Conference (ACSAC)}, keywords = {technical-reports}, month = {December}, publisher = {ACM}, title = {DR.SGX: Automated and Adjustable Side-Channel Protection for SGX using Data Location Randomization}, year = 2019 }
• Hands off my Database: Ransomware Detection in Databases through Dynamic Analysis of Query Sequences Iffländer, Lukas; Dmitrienko, Alexandra; Hagen, Christoph; Jobst, Michael; Kounev, Samuel; in ArXiv | arXiv:1907.06775v1 (2019).
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  @article{DBLP:journals/corr/abs-1907-06775, author = {Iffländer, Lukas and Dmitrienko, Alexandra and Hagen, Christoph and Jobst, Michael and Kounev, Samuel}, journal = {ArXiv | arXiv:1907.06775v1}, keywords = {sss-group}, month = {jul}, title = {Hands off my Database: Ransomware Detection in Databases through Dynamic Analysis of Query Sequences}, year = 2019 }

• POSTER: Efficient and Effective Ransomware Detection in Databases Hagen, Christoph; Dmitrienko, Alexandra; Iffländer, Lukas; Jobst, Michael; Kounev, Samuel; in 34th Annual Computer Security Applications Conference (ACSAC) (2018).
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  @inproceedings{Hagen2018_EfficientandEffectiveRansomwareDetectioninDatabases, author = {Hagen, Christoph and Dmitrienko, Alexandra and Iffländer, Lukas and Jobst, Michael and Kounev, Samuel}, booktitle = {34th Annual Computer Security Applications Conference (ACSAC)}, keywords = {sss-group}, month = {December}, organization = {ACM}, title = {POSTER: Efficient and Effective Ransomware Detection in Databases}, year = 2018 }
• SmarTor: Smarter Tor with Smart Contracts: Improving resilience of topology distribution in the Tor network. Greubel, Andre; Dmitrienko, Alexandra; Kounev, Samuel; in Annual Computer Security Applications Conference (ACSAC) (2018).
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  In the Tor anonymity network, the distribution of topology information relies on the correct behavior of five out of the nine trusted directory authority servers. This centralization is concerning since powerful adversary might compromise these servers and conceal in-formation about honest nodes, leading to the full de-anonymization of all Tor users. Our work aims at distributing the work of these trusted authorities, such increasing resilience against attacks on core infrastructure components of the Tor network. In particular,we leverage several emerging technologies, such as blockchains,smart contracts, and trusted execution environments to design and prototype a system called SmarTor. This system replaces the directory authorities with a smart contract and a distributed network of untrusted entities responsible for bandwidth measurements. We prototyped SmarTorusing Ethereum smart contracts and Intel SGX secure hardware. In our evaluation, we show that SmarTor produces significantly more reliable and precise measurements compared to the current measurement system. Overall, our solution improves the decentralization of the Tor network, reduces trust assumptions and increases resilience against powerful adversaries like law enforcement and intelligence services.

  @inproceedings{GrDmKo2018-ACSAC-SmarTor, abstract = {In the Tor anonymity network, the distribution of topology information relies on the correct behavior of five out of the nine trusted directory authority servers. This centralization is concerning since powerful adversary might compromise these servers and conceal in-formation about honest nodes, leading to the full de-anonymization of all Tor users. Our work aims at distributing the work of these trusted authorities, such increasing resilience against attacks on core infrastructure components of the Tor network. In particular,we leverage several emerging technologies, such as blockchains,smart contracts, and trusted execution environments to design and prototype a system called SmarTor. This system replaces the directory authorities with a smart contract and a distributed network of untrusted entities responsible for bandwidth measurements. We prototyped SmarTorusing Ethereum smart contracts and Intel SGX secure hardware. In our evaluation, we show that SmarTor produces significantly more reliable and precise measurements compared to the current measurement system. Overall, our solution improves the decentralization of the Tor network, reduces trust assumptions and increases resilience against powerful adversaries like law enforcement and intelligence services.}, author = {Greubel, Andre and Dmitrienko, Alexandra and Kounev, Samuel}, booktitle = {Annual Computer Security Applications Conference (ACSAC)}, keywords = {tor}, month = {December}, organization = {ACM}, title = {SmarTor: Smarter Tor with Smart Contracts: Improving resilience of topology distribution in the Tor network.}, year = 2018 }
• Hardening Bitcoin Against Off-Topic Data Inclusion Attacks Seeg, Andreas; Thesis; University of Würzburg. (2018, August).
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  Bitcoin is a novel peer to peer payment network that was started in 2009 and that has since flourished, gaining acceptance all over the world. By contributing their resourcefulness as network connectivity and computing power, all participants are creating the the Bitcoin network together. Designed as an open source software system, the participation rules for this network are not upheld by humans, but implemented in code, and generally allow anyone to join or leave the network at will.Since the time of Bitcoin’s inception, software errors had to be fixed and network attacks that tried to make parts of the network unavailable have been endured. This thesis provides a solution to the problem of illegal data inclusions within the blockchain, a data structure that cannot easily be changed and that has to be transmitted to participants of Bitcoin.The flaw, i.e. the possibility for any user of Bitcoin to potentially include illegal data and therefore illegalizes the whole blockchain, is not just a software flaw, but can be seen as a structural one if free participation and uncensorable payments are to be provided.The proposal to solve the problem is layered in two tiers. The first tier prevents effective data inclusion for some standard payment functions within Bitcoin. This allows participants to be certain that these transactions do not contain bad data, allowing them to be included into Bitcoin blocks. It also solves the problem of illegal data permanently occupying valuable resources on Bitcoin nodes, as parts of the provably clean transactions are generally held in RAM until they have been spent. The prevention mechanism is essentially a system that provides proof that bitcoins were sent to actual Bitcoin addresses,stopping data inclusion by address fields abusal.The second tier consists of rule changes for Bitcoin. First of all, all non-standard payment transactions are removed from Bitcoin, with the exception of the more computationally flexible and popular P2SH transactions. This limits the avenues for data inclusion in Bitcoin without destroying its flexibility or its smart contract capabilities. Then, P2SHtransactions are redesigned to allow for a more fine grained deletion of their data, so only offending parts could be removed without destroying the integrity of the rest. Finally, a special type of transaction is introduced that allows censorship of just the signature data of a P2SH transaction. This transaction has to include a payment equal to the Bitcoin provided by the previous P2SH transaction, however. By adding this requirement, the system is protected from becoming open to money creation attacks that might inflate the currency without the agreement of all participants.With these changes, illegal data inclusion becomes either impossible, or they can be re-moved without destroying the integrity of the payment system as a whole or for payments that have already happened.Implementing all proposed changes would go beyond the scope of a master thesis. Tier1 of the presented solution has been implemented as a proof-of-concept, though. As a basis, the most popular Bitcoin node software, Bitcoin Core, has been used. Additionally,the ideas behind tier 2 are explained in full, and the potential impact on the system is discussed.v This thesis also covers previous attempts to allow for data removal in similar systems. Their discussion is meant to encourage the adoption of the presented proposal, as it should causeless disruption than some more extreme measurements that might introduce centralization into a system that was meant to run decentralized.

  @mastersthesis{seeg2018hardening, abstract = {Bitcoin is a novel peer to peer payment network that was started in 2009 and that has since flourished, gaining acceptance all over the world. By contributing their resourcefulness as network connectivity and computing power, all participants are creating the the Bitcoin network together. Designed as an open source software system, the participation rules for this network are not upheld by humans, but implemented in code, and generally allow anyone to join or leave the network at will.Since the time of Bitcoin’s inception, software errors had to be fixed and network attacks that tried to make parts of the network unavailable have been endured. This thesis provides a solution to the problem of illegal data inclusions within the blockchain, a data structure that cannot easily be changed and that has to be transmitted to participants of Bitcoin.The flaw, i.e. the possibility for any user of Bitcoin to potentially include illegal data and therefore illegalizes the whole blockchain, is not just a software flaw, but can be seen as a structural one if free participation and uncensorable payments are to be provided.The proposal to solve the problem is layered in two tiers. The first tier prevents effective data inclusion for some standard payment functions within Bitcoin. This allows participants to be certain that these transactions do not contain bad data, allowing them to be included into Bitcoin blocks. It also solves the problem of illegal data permanently occupying valuable resources on Bitcoin nodes, as parts of the provably clean transactions are generally held in RAM until they have been spent. The prevention mechanism is essentially a system that provides proof that bitcoins were sent to actual Bitcoin addresses,stopping data inclusion by address fields abusal.The second tier consists of rule changes for Bitcoin. First of all, all non-standard payment transactions are removed from Bitcoin, with the exception of the more computationally flexible and popular P2SH transactions. This limits the avenues for data inclusion in Bitcoin without destroying its flexibility or its smart contract capabilities. Then, P2SHtransactions are redesigned to allow for a more fine grained deletion of their data, so only offending parts could be removed without destroying the integrity of the rest. Finally, a special type of transaction is introduced that allows censorship of just the signature data of a P2SH transaction. This transaction has to include a payment equal to the Bitcoin provided by the previous P2SH transaction, however. By adding this requirement, the system is protected from becoming open to money creation attacks that might inflate the currency without the agreement of all participants.With these changes, illegal data inclusion becomes either impossible, or they can be re-moved without destroying the integrity of the payment system as a whole or for payments that have already happened.Implementing all proposed changes would go beyond the scope of a master thesis. Tier1 of the presented solution has been implemented as a proof-of-concept, though. As a basis, the most popular Bitcoin node software, Bitcoin Core, has been used. Additionally,the ideas behind tier 2 are explained in full, and the potential impact on the system is discussed.v This thesis also covers previous attempts to allow for data removal in similar systems. Their discussion is meant to encourage the adoption of the presented proposal, as it should causeless disruption than some more extreme measurements that might introduce centralization into a system that was meant to run decentralized.}, author = {Seeg, Andreas}, keywords = {thesis_supervised_by_SSS_member}, month = {aug}, school = {University of Würzburg}, title = {Hardening Bitcoin Against Off-Topic Data Inclusion Attacks}, type = {Master Thesis}, year = 2018 }
• Design of a Shared Parking System with Special Attention to Security Aspects Englert, Simon; Thesis; University of Würzburg. (2018, August).
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  As major cities are growing more and more, the parking situation in those cities is becoming more precarious than ever. Parking demand already exceeds the limited amount of parking spaces available, and thus people searching for a place to park generate a significant part of those cities traffic. This leads to frustration with drivers, more traffic jams, unnecessary use of petrol and further air pollution. The creation of new parking spaces is often either difficult or very expansive, which means existing parking spots have to be used more efficiently. Solutions realizing that idea to try and solve those problems are for the most part called intelligent parking systems. This thesis is focused on Shared Parking, a special kind of intelligent parking system, which opens the possibility of sharing a parking place between different people. This work features the design and implementation of such a system with special attention given to security aspects.

  @mastersthesis{2018-BA-Simon-Engelert, abstract = {As major cities are growing more and more, the parking situation in those cities is becoming more precarious than ever. Parking demand already exceeds the limited amount of parking spaces available, and thus people searching for a place to park generate a significant part of those cities traffic. This leads to frustration with drivers, more traffic jams, unnecessary use of petrol and further air pollution. The creation of new parking spaces is often either difficult or very expansive, which means existing parking spots have to be used more efficiently. Solutions realizing that idea to try and solve those problems are for the most part called intelligent parking systems. This thesis is focused on Shared Parking, a special kind of intelligent parking system, which opens the possibility of sharing a parking place between different people. This work features the design and implementation of such a system with special attention given to security aspects.}, author = {Englert, Simon}, keywords = {thesis_supervised_by_sss_member}, month = {August}, school = {University of Würzburg}, title = {Design of a Shared Parking System with Special Attention to Security Aspects}, type = {Bachelor Thesis}, year = 2018 }
• DIMAQS - Dynamic Identification of Malicious Query Sequences Jobst, Michael; Thesis; University of Würzburg. (2018, June).
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  Ransomware is an emerging threat which imposed 5 billion USD loss in 2017 and is predicted to hit 11.5 billion in 2019. While initially targeting PC (client) platforms,recently it made a leap to server-side databases – in January 2017 we faced MongoDB Apocalypse attack, followed by other attack waves targeting a wide range of DB technologies such as MongoDB, MySQL, ElasticSearch, Cassandra, Hadoop, and CouchDB. While previous research has developed countermeasures against client-side ransomware (e.g., CryptoDrop and ShieldFS), no attention was given to the problem of server-side ransomware yet.This thesis aims to bridge this gap and presents design and implementation of the tool DIMAQS (Dynamic Identification of Malicious Query Sequences) for MySQL servers that can efficiently and effectively detect server-side ransomware. DIMAQS performs run-time monitoring of incoming queries and pattern matching using a Colored Petri Net (CPN) for attack detection. The system design of DIMAQS exhibits several novel techniques to enable efficient detection of malicious query sequences globally (i.e., without limiting detection to distinct user connections). Evaluation results show high efficiency with no false positives and no false negatives,and a very moderate performance overhead (for a run-time monitoring tool) of under5% in worst case scenarios.

  @mastersthesis{jobst2018dimaqs, abstract = {Ransomware is an emerging threat which imposed 5 billion USD loss in 2017 and is predicted to hit 11.5 billion in 2019. While initially targeting PC (client) platforms,recently it made a leap to server-side databases – in January 2017 we faced MongoDB Apocalypse attack, followed by other attack waves targeting a wide range of DB technologies such as MongoDB, MySQL, ElasticSearch, Cassandra, Hadoop, and CouchDB. While previous research has developed countermeasures against client-side ransomware (e.g., CryptoDrop and ShieldFS), no attention was given to the problem of server-side ransomware yet.This thesis aims to bridge this gap and presents design and implementation of the tool DIMAQS (Dynamic Identification of Malicious Query Sequences) for MySQL servers that can efficiently and effectively detect server-side ransomware. DIMAQS performs run-time monitoring of incoming queries and pattern matching using a Colored Petri Net (CPN) for attack detection. The system design of DIMAQS exhibits several novel techniques to enable efficient detection of malicious query sequences globally (i.e., without limiting detection to distinct user connections). Evaluation results show high efficiency with no false positives and no false negatives,and a very moderate performance overhead (for a run-time monitoring tool) of under5% in worst case scenarios.}, author = {Jobst, Michael}, keywords = {thesis_supervised_by_SSS_member}, month = {June}, school = {University of Würzburg}, title = {DIMAQS - Dynamic Identification of Malicious Query Sequences}, type = {Master Thesis}, year = 2018 }