Cyber-physical systems (CPS) have been increasingly attacked by hackers. CPS are especially vulnerable to attackers that have full knowledge of the system's configuration. Therefore, novel anomaly detection algorithms in the presence of a knowledgeable adversary need to be developed. However, this research is still in its infancy due to limited attack data availability and test beds. By proposing a holistic attack modeling framework, we aim to show the vulnerability of existing detection algorithms and provide a basis for novel sensor-based cyber-attack detection. Stealthy Attack GEneration (SAGE) for CPS serves as a tool for cyber-risk assessment of existing systems and detection algorithms for practitioners and researchers alike. Stealthy attacks are characterized by malicious injections into the CPS through input, output, or both, which produce bounded changes in the detection residue. By using the SAGE framework, we generate stealthy attacks to achieve three objectives: (i) Maximize damage, (ii) Avoid detection, and (iii) Minimize the attack cost. Additionally, an attacker needs to adhere to the physical principles in a CPS (objective iv). The goal of SAGE is to model worst-case attacks, where we assume limited information asymmetries between attackers and defenders (e.g., insider knowledge of the attacker). Those worst-case attacks are the hardest to detect, but common in practice and allow understanding of the maximum conceivable damage. We propose an efficient solution procedure for the novel SAGE optimization problem. The SAGE framework is illustrated in three case studies. Those case studies serve as modeling guidelines for the development of novel attack detection algorithms and comprehensive cyber-physical risk assessment of CPS. The results show that SAGE attacks can cause severe damage to a CPS, while only changing the input control signals minimally. This avoids detection and keeps the cost of an attack low. This highlights the need for more advanced detection algorithms and novel research in cyber-physical security.
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This content will become publicly available on October 12, 2024
Attack Recovery for Cyber-Physical Systems
Cyber-physical systems (CPSs) rely on computing components to control physical objects, and have been widely used in real-world life-critical applications. However, a CPS has security risks by nature due to the integration of many vulnerable subsystems, which adversaries exploit to inflict serious consequences. Among various attacks, sensor attacks pose a particularly significant threat, where an attacker maliciously modifies sensor measurements to drift system behavior. There is a lot of work in sensor attack prevention and detection. Nevertheless, an essential problem is overlooked: recovery--what to do after detecting a sensor attack, which needs to safely and timely bring a CPS back. We aim to highlight the need to investigate this problem, outline its four key challenges, and provide a brief overview of initial solutions in the field.
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- Award ID(s):
- 2333980
- NSF-PAR ID:
- 10499415
- Publisher / Repository:
- EAI
- Date Published:
- Journal Name:
- EAI International Conference on Security and Privacy in Cyber-Physical Systems and Smart Vehicles
- Format(s):
- Medium: X
- Location:
- Chicago, United States
- Sponsoring Org:
- National Science Foundation
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