Information flow for security in control systems

Conference · Thu Dec 01 00:00:00 EST 2016 · 2016 IEEE 55th Conference on Decision and Control (CDC)

DOI:https://doi.org/10.1109/cdc.2016.7799044· OSTI ID:1724574

Weerakkody, Sean; Sinopoli, Bruno; Kar, Soummya; Datta, Anupam

This paper considers the development of information flow analyses to support resilient design and active detection of adversaries in cyber physical systems (CPS). CPS security, though well studied, suffers from fragmentation. In this paper, we consider control systems as an abstraction of CPS. Here, we use information flow analysis, a well established set of methods developed in software security, to obtain a unified framework that captures and extends results in control system security. Specifically, we propose the Kullback Liebler (KL) divergence as a causal measure of information flow, which quantifies the effect of adversarial inputs on sensor outputs. We show that the proposed measure characterizes the resilience of control systems to specific attack strategies by relating the KL divergence to optimal detection. We then relate information flows to stealthy attack scenarios where an adversary can bypass detection. Finally, this article examines active detection mechanisms where a defender intelligently manipulates control inputs or the system itself to elicit information flows from an attacker's malicious behavior. In all previous cases, we demonstrate an ability to investigate and extend existing results through the proposed information flow analyses.

• Research Organization: Carnegie Mellon Univ., Pittsburgh, PA (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• DOE Contract Number: EE0007165
• OSTI ID: 1724574
• Journal Information: 2016 IEEE 55th Conference on Decision and Control (CDC), Conference: 2016 IEEE 55th Conference on Decision and Control (CDC), Las Vegas, NV, USA, 12-14 Dec. 2016
• Country of Publication: United States
• Language: English