The System Security Lab focuses on the security and privacy aspects of modern computing platforms, including software and hardware security, IoT security, AI security, and trustworthy computing. Our research aims to identify, analyze, and mitigate vulnerabilities in both traditional and emerging technologies, ensuring resilient and secure systems. We explore hardware-assisted security mechanisms, secure IoT architectures, AI-driven security models, and privacy-preserving computing to defend against evolving cyber threats. By integrating cryptographic techniques, secure hardware design, and AI security frameworks, we strive to build trustworthy, attack-resistant, and future-proof computing platforms.
HTW'25: High-Tech Women 2025
Join us for the fourth annual High-Tech Women Event (HTW’25) on September 4, 2025, in Darmstadt, Germany. This dynamic event provides a unique platform for accomplished women worldwide in technology and science to share their professional experiences and groundbreaking ideas
Successful PhD Defense: Phillip Rieger Completes PhD on Adversarially Robust Machine Learning
Successful PhD Defense: Phillip Rieger Completes PhD on Adversarially Robust Machine Learning
On May 14, 2025, Phillip Rieger successfully defended his doctoral dissertation titled “Training AI in Hostile Environments: Adversarially Robust Machine Learning”. Phillip has been an integral part of the System Security Lab since 2019, initially joining as a student assistant. In March 2020, just as the COVID-19 pandemic reshaped academic life, he began his PhD journey. Notably, he was the lab’s first pure AI-focused PhD student and helped establish the group’s AI research direction. Over the past five years, Phillip has significantly contributed to the lab’s research on Federated Learning (FL), developing robust defense mechanisms against adversarial attacks in distributed machine learning. His dissertation addresses key challenges in securing machine learning under adversarial conditions, introducing novel techniques to make FL systems resilient to poisoning and backdoor attacks, adaptive anomaly detection systems for IoT environments, and new defense strategies for split learning setups. Over the course of his PhD, Phillip published an impressive 19 papers, including 12 at Core A* conferences, and received two distinguished paper awards for his work on DeepFake detection and mitigating backdoor attacks in Split Learning. Beyond publications, he has led several workshops on FL and shared his expertise through a dedicated lecture series at TU Darmstadt on FL. Congratulations, Phillip, on this outstanding achievement, and all the best for your future!
Munich Satellite Navigation Summit 2025
Happy to be part of the Munich Satellite Navigation Summit 2025 which took place in the magnificent setting of the Munich Residenz