Robot resilience framework detects and mitigates cyber physical attacks
RobResilience: Implementing and Evaluating a Resilience Framework for Cyber-Physical Embodied Systems
Cryptography and SecurityRobotics
Summary
Cyber attacks on robots don't just steal data; they can harm the robot or people nearby. Many security systems spot attacks but don't know if the robot can still work safely despite problems. The authors built a system called RobResilience that checks if a robot can tolerate disruptions or safely degrade its performance during attacks. It uses a robot simulator and decides when to fix problems automatically. Their tests show RobResilience behaves as planned in different attack situations.
What this means in practice
- •For robotics developers: Implement runtime checks to keep robots operating safely despite cyber attacks by automatically triggering fixes when safety limits are breached.
- •For industrial automation teams: Improve safety of automated machinery by distinguishing safe performance degradation from critical failures during cyber intrusions.
Authors
Gysella Imrell, Emanuele Miotto, Mahya Mohammadi Kashani, Mauro Conti, Alberto Giaretta
Abstract
In embodied cyber-physical systems, active cyberattacks pose an immediate threat not just to data, but to physical integrity and human safety. While existing security approaches excel at detection, they lack the runtime mechanisms to determine whether a disruption is tolerable or if performance degradation remains within safe operational bounds. This gap leaves autonomous systems vulnerable to graceful failure paralysis, where they cannot distinguish between a safe, degraded state and a catastrophic hazard during an ongoing attack. This paper presents RobResilience, an implementation of a formal resilience framework for embodied cyber-physical systems in a Webots simulation environment, using a PR2 robot and ROS2. The framework evaluates three predicates at runtime: tolerable disruption ($δ$), tolerable degradation ($γ$), and mitigation feasibility ($μ$), over a compromised device set derived from IDS confidence scores. When resilience is lost, the framework triggers available mitigation strategies. We evaluate our implementation through eight attack scenarios that systematically cover all possible combinations of the predicate state space, varying attack targets, degradation rates, and mitigation availability. Results confirm that the runtime behaviour of the implementation is consistent with the theoretical definitions.