Collaborative Research: CPS: Medium: Sensor Attack Detection and Recovery in Cyber-Physical Systems
Lead PI:
Insup Lee
Co-PI:
Abstract

New vulnerabilities arise in Cyber-Physical Systems (CPS) as new technologies are integrated to interact and control physical systems. In addition to software and network attacks, sensor attacks are a crucial security risk in CPS, where an attacker alters sensing information to negatively interfere with the physical system. Acting on malicious sensor information can cause serious consequences. While many research efforts have been devoted to protecting CPS from sensor attacks, several critical problems remain unresolved. First, existing attack detection works tend to minimize the detection delay and false alarms at the same time; this goal, however, is not always achievable due to the inherent trade-off between the two metrics. Second, there has been much work on attack detection, yet a key question remains concerning what to do after detecting an attack. Importantly, a CPS should detect an attack and recover from the attack before irreparable consequences occur. Third, the interrelation between detection and recovery has met with insufficient attention: Integrating detection and recovery techniques would result in more effective defenses against sensor attacks.

This project aims to address these key problems and develop novel detection and recovery techniques. The project aims to achieve timely and safe defense against sensor attacks by addressing real-time adaptive-attack detection and recovery in CPS. First, this project explores new attack detection techniques that can dynamically balance the trade-off between the detection delay and the false-alarm rate in a data-driven fashion. In this way, the detector will deliver attack detection with predictable delay and maintain the usability of the detection approach. Second, this project pursues new recovery techniques that bring the system back to a safe state before a recovery deadline while minimizing the degradation to the mission being executed by the system. Third, this project investigates efficient techniques that address the attack detection and recovery in a coordinated fashion to significantly improve response to attacks. Specific research tasks include the development of real-time adaptive sensor attack detection techniques, real-time attack recovery techniques, and attack detection and recovery coordination techniques. The developed techniques will be implemented and evaluated on multiple CPS simulators and an autonomous vehicle testbed.

Performance Period: 07/15/2022 - 06/30/2025
Institution: University of Pennsylvania
Award Number: 2143274