2025/09/15 by James Ward, Ward, James C., Alex J. Bott +5
Computer Science · #Advanced Malware Detection Techniques #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Robotics (cs.RO) #Software Testing and Debugging Techniques
paper · pdf · doi:10.48550/arxiv.2509.11971
openalex publication_date 2025/09/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Simulating hostile attacks of physical autonomous systems can be a useful tool to examine their robustness to attack and inform vulnerability-aware design. In this work, we examine this through the lens of multi-robot patrol, by presenting a machine learning-based adversary model that observes robot patrol behavior in order to attempt to gain undetected access to a secure environment within a limited time duration. Such a model allows for evaluation of a patrol system against a realistic potential adversary, offering insight into future patrol strategy design. We show that our new model outperforms existing baselines, thus providing a more stringent test, and examine its performance against multiple leading decentralized multi-robot patrol strategies.