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Industrial robots are prototypical cyber-physical systems which operate according to the operation code and are monitored in real-time based on their movement data. However, industrial robots suffer from replay attacks, via which attackers can manipulate the robot operation without being observed by the monitoring system. To mitigate this vulnerability, we design a novel intrusion detection system for industrial robots using their power fingerprint, called PIDS (Power-based Intrusion Detection System), and deliver PIDS as a bump-in-the-wire module installed at the powerline of commodity robots. The foundation of PIDS is the physically-induced dependency between the robot movement and the concomitant power consumption, which PIDS captures via joint physical analysis and (cyber) data-driven modeling. PIDS then fingerprints the robot movements observed by the monitoring system using their expected power consumption, and cross-validates the fingerprints with empirically collected power information a mismatch thereof flags anomalies of the observed movements. We have evaluated PIDS using three models of robots from different vendors i.e., ABB IRB120, KUKA KR6 R700, and Universal Robots UR5 robots with over 2,000 operation cycles. Experimental results show that PIDS detects replay attacks at an average rate of 96.5% (up to 99.9%) and a 0.1s latency.

Industrial robots have been shown to suffer from replay attacks, via which adversaries not only manipulate the robot operation by downloading malicious code, but also prevent the detection of this manipulation by replaying recorded (and normal) movement data to the monitoring system. To protect industrial robots from replay attacks, we design a novel intrusion detection system using the power fingerprint of robots, called PIDS (Power-based Intrusion Detection System), and deliver PIDS as a bump-in-the-wire module installed at the powerline of commodity robots. The foundation of PIDS is the physically-induced dependency between the robot movement and the concomitant electrical power consumption, which PIDS captures via joint physical analysis and (cyber) data-driven modeling. PIDS then fingerprints the robot movements observed by the monitoring system using their expected power consumption, and cross-validates the fingerprints with empirically collected power information --- a mismatch thereof flags anomalies of the observed movements (i.e., evidence of replay attack). We have evaluated PIDS using three models of robots from different vendors --- i.e., ABB IRB120, KUKA KR6 R700, and Universal Robots UR5 robots --- with over 2, 000 operation cycles. The experimental results show that PIDS detects replay attacks with an average rate of 96.5% (up to 99.9%) and a 0.1s latency.