Advancing Cyber-Physical Systems Resilience: The Effects of Evolving Disruptions

Abstract

Highly interconnected cyber-physical systems (CPSs) and complex networks can enable greater production capabilities and quality of life through their cyber-augmented characteristics. The high level of interconnectedness within CPSs, however, also increases their vulnerabilities to intelligent malware and coordinated attacks. A disrupted part of a CPS can not only cease to function properly, it can also increase the vulnerability of other parts of the CPS due to the interconnections. In this research, we expand on our previous work, the Collaborative Response against Disruption Propagation (CRDP) model and explore the effects of intelligently coordinated attacks on CPSs. This work addresses a severe limitation of the CRDP model: The assumption that disruptions have random targets. In this new work, the adversarial disruption network initially has only limited information about the target CPS network and assumes random targets. Over time, the adversarial disruption network is assumed to utilize decision tree learning algorithm to intelligently learn about the CPS’s response capabilities over time. Then, the disruption network utilizes this learning to improve its attacks by the defense targeting and response protocols. The novel adversarial disruption learning and targeting protocols are simulated and validated with a random network model. The experiments show that the developed protocols defending the disrupted network can significantly stress the CPS’s resilience capabilities. Further research is needed to understand how the role of cyber-collaborative protocols can advance the CPS resilience.