Abstract: We propose an adversarial attack for machine-learning-based network intrusion detection systems that selectively alters only the most influential features. Unlike conventional attacks such ...

Abstract: Machine learning plays a crucial role in autonomous vehicles, particularly in driver assistance technologies that enhance driving efficiency or eliminate the need for human intervention. One ...

The project sits at the intersection of privacy-preserving machine learning, distributed systems, and trustworthy AI, with implications for regulatory compliance and real-world deployment of federated ...

Large language models are inherently vulnerable to prompt injection attacks, and no amount of hardening will ever fully close that gap. The imbalance between available attacks and available ...