Centralized Versus Decentralized Detection of Attacks in Stochastic Interconnected Systems

We consider a security problem for interconnected systems governed by linear, discrete, time-invariant, stochastic dynamics, where the objective is to detect exogenous attacks by processing the measurements at different locations. We consider two classes of detectors, namely centralized and decentralized detectors, which differ primarily in their knowledge of the system model. In particular, a decentralized detector has a model of the dynamics of the isolated subsystems, but is unaware of the interconnection signals that are exchanged among subsystems. Instead, a centralized detector has a model of the entire dynamical system. We characterize the performance of the two detectors and show that, depending on the system and attack parameters, each of the detectors can outperform the other. In particular, it may be possible for the decentralized detector to outperform its centralized counterpart, despite having less information about the system dynamics, and this surprising property is due to the nature of the considered attack detection problem. To complement our results on the detection of attacks, we propose and solve an optimization problem to design attacks that maximally degrade the system performance while maintaining a pre-specified degree of detectability. Finally, we validate our findings via numerical studies on an electric power system.Comment: Submitted to IEEE Transactions on Automatic Control (TAC

Extremum Seeking Based Fault-Tolerant Cooperative Control for Multiagent Systems

We propose a novel fault-tolerant cooperative control strategy for multiagent systems. A set of unknown input observers for each agent are constructed for fault detection. Then a real-time adaptive extremum seeking algorithm is utilized for adaptive approximation of fault parameter. We prove that the consensus can be still reached by regulating the interconnection weights and changing the connection topology of the fault agent. A numerical simulation example is given to illustrate the feasibility and effectiveness of the proposed method

A Distributed Approach for the Detection of Covert Attacks in Interconnected Systems with Stochastic Uncertainties

The design of a distributed architecture for the detection of covert attacks in interconnected Cyber-Physical Systems is addressed in this paper, in the presence of stochastic uncertainties. By exploiting communication between neighbors, the proposed scheme allows for the detection of covert attacks that are locally stealthy. The proposed methodology adopts a decentralized filter, jointly estimating the local state and the aggregate effect of the physical interconnections, and uses the communicated estimates to obtain an attack-sensitive residual. We derive some theoretical detection properties for the proposed architecture, and present numerical simulations