Strengthening Cyber Defenses for Networked Autonomous Robots

A compact testbed for assessing cyber-attacks and defenses for autonomous mobile robots is developed, using the TurtleBot3 (TB3) platform. The TB3 is set up to perform tracking tasks in the testbed. We then implement three classes of attacks targeting camera, LiDAR, and position sensors through e.g. spoofing, temporal delay, and replay, respectively. Our experimental setup demonstrates that these attacks can compromise robot tracking performance. To address these vulnerabilities, we develop and validate three lightweight de-fense mechanisms: dynamic watermarking for camera defense, velocity consistency checking for LiDAR validation, and model-based variance monitoring for position attack detection. Experiments have been conducted to assess attack impacts and defense implications. For instance, experiments have shown that replay attacks can cause position errors exceeding 0.4m within 15 seconds. Likewise, experiments indicate that our LiDAR defense achieves detection within 200ms. The testbed provides a practical framework for evaluating cyber-physical vulnerabilities and defense strategies in autonomous robotic systems.