Raphael Götz

Chair of Software Engineering (Informatik II)
Department of Computer Science
University of Würzburg

Am Hubland, 97074, Würzburg, Germany
Informatikgebäude, 1.OG, Room A117

Phone: +49 931 31-85619

Occupation

Since February 2023 : PhD student in the Secure Software Systems Group at the Chair of Computer Science II - Software-Engineering, University of Würzburg

Research interests

Supply chain security
Security in large-scale machine learning
Targeted poisoning attacks on deep learning

Publications

2023[ to top ]

Ensuring Integrity of NVMe Offloaded Data in Large-Scale Machine Learning. Götz, Raphael; Thesis; Master Thesis. (2023, January).
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Machine learning is increasingly used in security-critical domains and therefore has become an attractive target for attackers. In a targeted poisoning attack, the machine learning model is trained to behave normally on benign input, however, when a certain trigger is present in the input, an attacker-chosen misbehavior is triggered. At the same time, neural networks are constantly increasing in size, especially in the natural language processing domain, because more parameters can achieve higher accuracy. In recent years, there have been impressive innovations that have led to networks such as the Megatron-Turing NLG 530B with 530 billion parameters. DeepSpeed is an open-source deep learning optimization library that enables the training of such large networks. One of many innovations it implements is a Non-Volatile Memory Express (NVMe) offload that increases memory efficiency by moving data from expensive GPU and CPU memory to cheap NVMe memory. However, this mechanism opens a potential attack surface that could be exploited to perform poisoning attacks. Therefore, this thesis investigates the security of the NVMe offload mechanism and improves it. To achieve this, first, untargeted poisoning attack scenarios are tested to show that the NVMe offload is actually vulnerable. Then a security extension, able to guarantee the integrity and freshness of the data offloaded to the NVMe is designed and extensively evaluated. During this process, various trade-offs between security and performance impact are carefully considered through the implementation and benchmarking of several different versions of the extension. Based on the experimental results, the security extension is then further improved. Furthermore, this thesis also investigates if multithreading and the use of previously generated hash tables can reduce the performance impact.

@mastersthesis{gotz2023ensuring, abstract = {Machine learning is increasingly used in security-critical domains and therefore has become an attractive target for attackers. In a targeted poisoning attack, the machine learning model is trained to behave normally on benign input, however, when a certain trigger is present in the input, an attacker-chosen misbehavior is triggered. At the same time, neural networks are constantly increasing in size, especially in the natural language processing domain, because more parameters can achieve higher accuracy. In recent years, there have been impressive innovations that have led to networks such as the Megatron-Turing NLG 530B with 530 billion parameters. DeepSpeed is an open-source deep learning optimization library that enables the training of such large networks. One of many innovations it implements is a Non-Volatile Memory Express (NVMe) offload that increases memory efficiency by moving data from expensive GPU and CPU memory to cheap NVMe memory. However, this mechanism opens a potential attack surface that could be exploited to perform poisoning attacks. Therefore, this thesis investigates the security of the NVMe offload mechanism and improves it. To achieve this, first, untargeted poisoning attack scenarios are tested to show that the NVMe offload is actually vulnerable. Then a security extension, able to guarantee the integrity and freshness of the data offloaded to the NVMe is designed and extensively evaluated. During this process, various trade-offs between security and performance impact are carefully considered through the implementation and benchmarking of several different versions of the extension. Based on the experimental results, the security extension is then further improved. Furthermore, this thesis also investigates if multithreading and the use of previously generated hash tables can reduce the performance impact.}, author = {Götz, Raphael}, keywords = {sssgroup}, month = {01}, publisher = {Master Thesis}, school = {University of Würzburg}, title = {Ensuring Integrity of NVMe Offloaded Data in Large-Scale Machine Learning}, type = {Master Thesis}, year = 2023 }
Security of NVMe Offloaded Data in Large-Scale Machine Learning. Krauß, Torsten; Götz, Raphael; Dmitrienko, Alexandra; in European Symposium on Research in Computer Security (ESORICS) (2023).
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@article{krauss2024security, author = {Krauß, Torsten and Götz, Raphael and Dmitrienko, Alexandra}, journal = {European Symposium on Research in Computer Security (ESORICS)}, keywords = {Private_AI}, title = {Security of NVMe Offloaded Data in Large-Scale Machine Learning}, year = 2023 }

2020[ to top ]

Survey on the Evaluation of Self-Aware Computing Systems. Götz, Raphael; Krupitzer, Christian; Kounev, Samuel; (2020).
- [ Abstract ]
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Software is becoming larger and more complex. In 2001 IBM published a manifest in which they stated that the increase in complexity is a major obstacle to progress for the IT industry. A solution to this looming complexity crisis is self-aware computing systems. These systems learn models that, help them to capture knowledge on themselves and their environment, and use these to consider actions in accoradnce with to act in accordance with higher level goals [3]. This enables them to act according to their knowledge and reason. One open issue in the field of self-aware computing systems is the growing demand for evaluation methods that make these systems comparable [4]. The main problem when evaluating self-aware computing systems is that traditional software metrics, such as re- sponse time or throughput, are not sufficient, as they cannot capture all relevant aspects of self-awareness. This thesis addresses the problems and challenges of evaluating self-aware computing systems and their related concepts, by providing an overview over the currently available evaluation methods for these systems. For this purpose, 37 papers dealing with the evaluation of self-aware computing systems and their related concepts are analyzed and classified using a taxonomy. This answers five research questions. Namely, the questions how and when the evaluation of self-aware computing systems is performed, and which parts of the systems are evaluated most frequently. The question whether the available methods of evaluation do address overshooting, oscillations an the level of self-awareness is also addressed. Answering these research questions results in the identification of research gaps. Namely, the insight that there is a lack of research concerned with the evaluation of the design of self-aware systems and proactive adaptation. Other observations are, that the sets of metrics and quality attributes found in the literature have surprisingly little overlap, suggesting that the do not cover all the relevant facets of self-adaptation and self-awareness, and that the evaluation of the level of self-awareness without access to the internals of the system is still rather difficult. Based on these insights improvements and best practices for future research are suggested.

@mastersthesis{noauthororeditor, abstract = {Software is becoming larger and more complex. In 2001 IBM published a manifest in which they stated that the increase in complexity is a major obstacle to progress for the IT industry. A solution to this looming complexity crisis is self-aware computing systems. These systems learn models that, help them to capture knowledge on themselves and their environment, and use these to consider actions in accoradnce with to act in accordance with higher level goals [3]. This enables them to act according to their knowledge and reason. One open issue in the field of self-aware computing systems is the growing demand for evaluation methods that make these systems comparable [4]. The main problem when evaluating self-aware computing systems is that traditional software metrics, such as re- sponse time or throughput, are not sufficient, as they cannot capture all relevant aspects of self-awareness. This thesis addresses the problems and challenges of evaluating self-aware computing systems and their related concepts, by providing an overview over the currently available evaluation methods for these systems. For this purpose, 37 papers dealing with the evaluation of self-aware computing systems and their related concepts are analyzed and classified using a taxonomy. This answers five research questions. Namely, the questions how and when the evaluation of self-aware computing systems is performed, and which parts of the systems are evaluated most frequently. The question whether the available methods of evaluation do address overshooting, oscillations an the level of self-awareness is also addressed. Answering these research questions results in the identification of research gaps. Namely, the insight that there is a lack of research concerned with the evaluation of the design of self-aware systems and proactive adaptation. Other observations are, that the sets of metrics and quality attributes found in the literature have surprisingly little overlap, suggesting that the do not cover all the relevant facets of self-adaptation and self-awareness, and that the evaluation of the level of self-awareness without access to the internals of the system is still rather difficult. Based on these insights improvements and best practices for future research are suggested.}, author = {Götz, Raphael and Krupitzer, Christian and Kounev, Samuel}, keywords = {raphaelgoetz}, title = {Survey on the Evaluation of Self-Aware Computing Systems}, year = 2020 }

Occupation

Research interests

Publications

Data privacy protection

Data privacy protection

Picture credits