Foundations of a CPS Resilience - July 2018

PI: Xenofon Koutsoukos

HARD PROBLEM(S) ADDRESSED

The goals of this project are to develop the principles and methods for designing and analyzing resilient CPS architectures that deliver required service in the face of compromised components. A fundamental challenge is to understand the basic tenets of CPS resilience and how they can be used in developing resilient architectures. The primary hard problem addressed is resilient architectures. In addition, the work scalability and composability as well as metrics and evaluation.

PUBLICATIONS

Bradley Potteiger, Zhenkai Zhang and Xenofon Koutsoukos. "Integrated Instruction Set Randomization and Control Reconfiguration for Securing Cyber-Physical Systems", Symposium and Bootcamp on the Science of Security, HotSoS 2018, Raleigh, NC, April 10-11, 2018.

Jiani Li and Xenofon Koutsoukos. "Resilient Distributed Diffusion for Multi-task Estimation", The 14th International Conference on Distributed Computing in Sensor Systems (DCOSS 2018), Bronx, NY, June 18-20, 2018.

KEY HIGHLIGHTS

This quarterly report presents two key highlights that demonstrate the foundations of CPS resilience by integrating redundancy, diversity, and hardening. The highlights address resilient control and state estimation respectively.

Highlight 1: An integrated moving target defense and control reconfiguration approach for securing CPS

Cyber-Physical Systems (CPS) have been increasingly subject to cyber-attacks including code injection attacks. With the tightly coupled nature of cyber components with the physical domain, these attacks have the potential to cause significant damage if safety-critical applications such as automobiles are compromised. Moving target defense techniques such as instruction set randomization (ISR) have been commonly proposed to address these types of attacks. However, under current implementations an attack can result in system crashing which is unacceptable in CPS. As such, CPS necessitate proper control reconfiguration mechanisms to prevent a loss of availability in system operation. Our work addresses the problem of maintaining system and security properties of a CPS under attack by integrating ISR, detection, and recovery capabilities that ensure safe, reliable, and predictable system operation. Specifically, we consider the problem of detecting code injection attacks and reconfiguring the controller in real-time. The developed framework is demonstrated with an autonomous vehicle case study [1].

Bradley Potteiger, Zhenkai Zhang and Xenofon Koutsoukos. "Integrated Instruction Set Randomization and Control Reconfiguration for Securing Cyber-Physical Systems", Symposium and Bootcamp on the Science of Security, HotSoS 2018, Raleigh, NC, April 10-11, 2018.

Highlight 2: Resilient distributed diffusion for multi-task estimation

Distributed diffusion is a powerful algorithm for multi-task state estimation which enables networked agents to interact with neighbors to process input data and diffuse information across the network. Compared to a centralized approach, diffusion offers multiple advantages that include robustness to node and link failures. We consider distributed diffusion for multi-task estimation where networked agents must estimate distinct but correlated states of interest by processing streaming data. By exploiting the adaptive weights used for diffusing information, we develop attack models that drive normal agents to converge to states selected by the attacker. The attack models can be used for both stationary and nonstationary state estimation. In addition, we develop a resilient distributed diffusion algorithm under the assumption that the number of compromised nodes in the neighborhood of each normal node is bounded by F and we show that resilience may be obtained at the cost of performance degradation. Finally, we evaluate the proposed attack models and resilient distributed diffusion algorithm using stationary and non-stationary multi-target localization [2].

Jiani Li and Xenofon Koutsoukos. "Resilient Distributed Diffusion for Multi-task Estimation", The 14th International Conference on Distributed Computing in Sensor Systems (DCOSS 2018), Bronx, NY, June 18-20, 2018.

COMMUNITY ENGAGEMENTS

Our research was presented in two conferences: HotSoS 2018 and DCOSS 2018.

EDUCATIONAL ADVANCES: