Optimal completely stealthy attacks against remote estimation in cyber-physical systems

Optimal Strictly Stealthy Attack Design on Cyber-Physical Systems: A Data-Driven Approach

In this article, an issue of data-driven optimal strictly stealthy attack design for the stochastic linear invariant systems is investigated, with the aim of maximizing the system performance degradation under an energy bounded constraint and bypassing the parity-space-based attack detector. Importantly, the proposed attack policy refrains from the assumption that the system knowledge is known to attackers. A novel strictly stealthy attack sequence (SSAS), coordinating the sensor and actuator signals simultaneously, is proposed with a sufficient and necessary condition for the existence of such an attack presented. Specifically, the SSAS is parameterized as a vector in the null space of a specific matrix which is constructed by a parity matrix and the system Markov parameters. For the purpose of data-driven attack realization, modified subspace identification methods are utilized to achieve an unbiased estimation of the required parameters via the closed-loop data. On this basis, the attack design is formulated as a constrained optimization problem, an explicit solution to which is given to characterize the optimal strictly stealthy attack. Finally, the vulnerability of the cyber-physical systems is analysed from the perspective of the parameter selection for the parity space-based detector. A case study on a three-tank model verifies the efficiency of the proposed approach.

Optimal energy constrained deception attacks in cyber-physical systems with multiple channels: A fusion attack approach

This paper studies the issue of developing the optimal deception attacks on the multiple channels in cyber-physical systems, where the attackers are limited by energy constraints. To fully utilize the eavesdropped data, by linearly combining the innovations from the different channels, a fusion attack model is proposed under the stealthiness condition. According to the statistical characteristics of the correlated stochastic variables and the orthogonality principle, the state estimation error is quantified and analyzed by deriving the iteration of the error covariance matrices of the remote estimators under the proposed attack framework. Moreover, by analyzing the correlations of the decision variables in the objective function, it is shown that the attack parameters and energy allocation strategy can be derived by two steps without loss of optimality, such that the optimal attack scheme is acquired by solving a multivariate semi-definite programming (SDP) problem and a linear 0-1 programming problem respectively. Finally, simulation examples are provided to illustrate the effectiveness of the proposed method.

Fusion and detection for multi-sensor systems under false data injection attacks

This article investigates a security problem in cyber-physical systems under multisensory framework. Data packets from each sensor are transmitted to a remote estimator through wireless channels which could be attacked by a attacker and injected false data. Based on the data of reliable sensors and the relativity between reliable and unreliable sensors, a revised multi-sensor Kalman filter fusion algorithm is proposed. Under the proposed algorithm, an optimal linear false data injection attack strategy which is more general with an arbitrary mean of Gaussian distribution is designed. To further improve the detection performance, an Expected SARSA-based attack detection algorithm is proposed. Finally, simulation results based on an unmanned aerial vehicle are provided to illustrate the feasibility and efficiency of the obtained results.