期刊
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
卷 17, 期 -, 页码 670-683出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIFS.2022.3146078
关键词
Attack design; distributed estimation; CPS security; false data injection attack; Kalman-consensus filter; stochastic approximation
资金
- Professional Development Allowance (PDA) of IIT Delhi
- I-Hub Foundation for Cobotics (IHFC)
- Office of Naval research (ONR) [N00014-15-1-2550]
- NSF [CCF-1817200, CCF-2008927]
- Army Research Office (ARO) [W911NF1910269]
- DOE [DE-SC0021417]
- Swedish Research Council [2018-04359]
- ONR [503400-78050]
- Ericsson Research Foundation
- Swedish Research Council [2018-04359] Funding Source: Swedish Research Council
- U.S. Department of Defense (DOD) [W911NF1910269] Funding Source: U.S. Department of Defense (DOD)
- U.S. Department of Energy (DOE) [DE-SC0021417] Funding Source: U.S. Department of Energy (DOE)
- Vinnova [2018-04359] Funding Source: Vinnova
This study examines the design of false data injection attacks on a distributed cyber-physical system, utilizing a stochastic process with linear dynamics and Gaussian noise. The attack involves manipulating sensor observations and messages among agent nodes to steer estimates towards a specified value while maintaining attack detection probability constraints.
Herein, design of false data injection attack on a distributed cyber-physical system is considered. A stochastic process with linear dynamics and Gaussian noise is measured by multiple agent nodes, each equipped with multiple sensors. The agent nodes form a multi-hop network among themselves. Each agent node computes an estimate of the process by using its sensor observation and messages obtained from neighboring nodes, via Kalman-consensus filtering. An external attacker, capable of arbitrarily manipulating the sensor observations of some or all agent nodes, injects errors into those sensor observations. The goal of the attacker is to steer the estimates at the agent nodes as close as possible to a pre-specified value, while respecting a constraint on the attack detection probability. To this end, a constrained optimization problem is formulated to find the optimal parameter values of a certain class of linear attacks. The parameters of linear attack are learnt on-line via a combination of stochastic approximation based update of a Lagrange multiplier, and an optimization technique involving either the Karush-Kuhn-Tucker (KKT) conditions or online stochastic gradient descent. The problem turns out to be convex for some special cases. Desired convergence of the proposed algorithms are proved by exploiting the convexity and properties of stochastic approximation algorithms. Finally, numerical results demonstrate the efficacy of the attack.
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