A Tractable Fault Detection and Isolation Approach for Nonlinear Systems with Probabilistic Performance

This article presents a novel perspective along with a scalable methodology to design a fault detection and isolation (FDI) filter for high dimensional nonlinear systems. Previous approaches on FDI problems are either confined to linear systems or they are only applicable to low dimensional dynamics with specific structures. In contrast, shifting attention from the system dynamics to the disturbance inputs, we propose a relaxed design perspective to train a linear residual generator given some statistical information about the disturbance patterns. That is, we propose an optimization-based approach to robustify the filter with respect to finitely many signatures of the nonlinearity. We then invoke recent results in randomized optimization to provide theoretical guarantees for the performance of the proposed filer. Finally, motivated by a cyber-physical attack emanating from the vulnerabilities introduced by the interaction between IT infrastructure and power system, we deploy the developed theoretical results to detect such an intrusion before the functionality of the power system is disrupted

Optimal Attack against Cyber-Physical Control Systems with Reactive Attack Mitigation

This paper studies the performance and resilience of a cyber-physical control system (CPCS) with attack detection and reactive attack mitigation. It addresses the problem of deriving an optimal sequence of false data injection attacks that maximizes the state estimation error of the system. The results provide basic understanding about the limit of the attack impact. The design of the optimal attack is based on a Markov decision process (MDP) formulation, which is solved efficiently using the value iteration method. Using the proposed framework, we quantify the effect of false positives and mis-detections on the system performance, which can help the joint design of the attack detection and mitigation. To demonstrate the use of the proposed framework in a real-world CPCS, we consider the voltage control system of power grids, and run extensive simulations using PowerWorld, a high-fidelity power system simulator, to validate our analysis. The results show that by carefully designing the attack sequence using our proposed approach, the attacker can cause a large deviation of the bus voltages from the desired setpoint. Further, the results verify the optimality of the derived attack sequence and show that, to cause maximum impact, the attacker must carefully craft his attack to strike a balance between the attack magnitude and stealthiness, due to the simultaneous presence of attack detection and mitigation

Detection and Characterization of Actuator Attacks Using Kalman Filter Estimation

In this thesis, two discrete-time control systems subject to noise, are modeled, analyzed and estimated. These systems are then subjected to attack by false signals such as constant and ramp signals. In order to find out how and when the control systems are being attacked by the false signals, several detection algorithms are applied to the systems. This work focuses on actuator attack detection. To detect the presence of false actuator signals, a bank of Kalman filters is set up which uses adaptive estimation and conditional probability density functions for detecting the false signals. The individual Kalman filters are each tuned to satisfy a control system: one of which is the original system and the other of which is the system with a false signal. The use of the bank of Kalman filters to detect actuator attacks is tested in 4 cases; first-order system attacked by a constant or ramp signal and then a second-order system subject to the same types of attack signals. This work shows the bank of Kalman filters can successfully detect the intrusion of false signals for actuator attack by using several different detection algorithms. Simulations show that the false signal is found and detected in all cases

A virtual actuator approach for the secure control of networked LPV systems under pulse-width modulated DoS attacks

In this paper, we formulate and analyze the problem of secure control in the context of networked linear parameter varying (LPV) systems. We consider an energy-constrained, pulse-width modulated (PWM) jammer, which corrupts the control communication channel by performing a denial-of-service (DoS) attack. In particular, the malicious attacker is able to erase the data sent to one or more actuators. In order to achieve secure control, we propose a virtual actuator technique under the assumption that the behavior of the attacker has been identified. The main advantage brought by this technique is that the existing components in the control system can be maintained without need of retuning them, since the virtual actuator will perform a reconfiguration of the plant, hiding the attack from the controller point of view. Using Lyapunov-based results that take into account the possible behavior of the attacker, design conditions for calculating the virtual actuators gains are obtained. A numerical example is used to illustrate the proposed secure control strategy.Peer ReviewedPostprint (author's final draft

Detection and Characterization of Actuator Attacks Using Kalman Filter Estimation

In this thesis, two discrete-time control systems subject to noise, are modeled, analyzed and estimated. These systems are then subjected to attack by false signals such as constant and ramp signals. In order to find out how and when the control systems are being attacked by the false signals, several detection algorithms are applied to the systems. This work focuses on actuator attack detection. To detect the presence of false actuator signals, a bank of Kalman filters is set up which uses adaptive estimation and conditional probability density functions for detecting the false signals. The individual Kalman filters are each tuned to satisfy a control system: one of which is the original system and the other of which is the system with a false signal. The use of the bank of Kalman filters to detect actuator attacks is tested in 4 cases; first-order system attacked by a constant or ramp signal and then a second-order system subject to the same types of attack signals. This work shows the bank of Kalman filters can successfully detect the intrusion of false signals for actuator attack by using several different detection algorithms. Simulations show that the false signal is found and detected in all cases