Secure Adaptive-Event-Triggered Filter Design With Input Constraint and Hybrid Cyber Attack

Distributed Consensus Estimation for Networked Multi-Sensor Systems under Hybrid Attacks and Missing Measurements

Cyber-security research on networked multi-sensor systems is crucial due to the vulnerability to various types of cyberattacks. For the development of effective defense measures, attention is required to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker's perspective. This paper aims to tackle the problem of distributed consensus estimation for networked multi-sensor systems subject to hybrid attacks and missing measurements. To account for both random denial of service (DoS) attacks and false data injection (FDI) attacks, a hybrid attack model on the estimator-to-estimator communication channel is presented. The characteristics of missing measurements are defined by random variables that satisfy the Bernoulli distribution. Then a modified consensus-based distributed estimator, integrated with the characteristics of hybrid attacks and missing measurements, is presented. For reducing the computational complexity of the optimal distributed estimation method, a scalable suboptimal distributed consensus estimator is designed. Sufficient conditions are further provided for guaranteeing the stability of the proposed suboptimal distributed estimator. Finally, a simulation experiment on aircraft tracking is executed to validate the effectiveness and feasibility of the proposed algorithm.

Optimal Stealthy Attacks With Energy Constraint Against Remote State Estimation

This article investigates the problem of energy-constrained stealthy attack strategy against remote state estimation for cyber-physical systems. Taking into account the energy constraint, the malicious attacker is required to schedule the off-line generated signals to modify the transmitted data with limited times over a finite-time horizon under the stealthiness condition, which makes the design of attack strategy more complex. Different from the attack schedules which are studied on the basis of prescribed attack signals in the existing results, the attack strategy is presented under the framework of collaborative design to deteriorate the estimation performance to the largest extent, which yet leads to the coupling between the attack schedules and attack signals. To overcome the difficulty without sacrificing the optimality, the attack design problem is solved in two steps. First, analyze the problem with the given attack schedule to derive the optimal attack signals. Then, the optimal schedule is obtained by efficiently solving the nonlinear 0-1 programming problem based on the algorithm of reducing the search space which is designed to eliminate a part of the nonoptimal solutions. To demonstrate the theoretical results, a simulation example is provided.

Distributed H∞ filtering of replay attacks over sensor networks

A distributed H∞ filtering issue is addressed in this paper for discrete-time nonlinear systems in the face of replay attacks over sensor networks, where an indicator variable is introduced to describe whether a replay attack is launched by the adversaries. First, an interesting pattern dependent on three parameters including a time-varying one is established to account for the temporal behavior of malicious attacks. Then, taking advantage of such a model, the resulting filter dynamic is transformed into a switching system with a subsystem with time-varying delays. By means of the famous switching system theory, a sufficient condition guaranteeing the H∞ performance is derived to disclose the tolerant attack condition, that is, the attack-active duration and proportion. In addition, the applicable filter gains are achieved with the aid of the solutions of matrix inequalities. Finally, an example is purposively given to adequately illustrate the availability of the developed secure filtering strategy.

Chance-Constrained H∞ State Estimation for Recursive Neural Networks Under Deception Attacks and Energy Constraints: The Finite-Horizon Case

In this article, the chance-constrained H∞ state estimation problem is investigated for a class of time-varying neural networks subject to measurements degradation and randomly occurring deception attacks. A novel energy-constrained deception attack model is proposed, in which both the occurrence of the attack and the selection of released faked packet are random and the energy of the deception attack is introduced, calculated, and analyzed quantitatively. The main purpose of the addressed problem is to design an H∞ estimator such that the prefixed probabilistic constraints of the system error dynamics are satisfied and the H∞ performance is also ensured. Subsequently, the explicit expression of the estimator gains is derived by solving a minimization problem subjected to certain recursive inequality constraints. Finally, a numerical example and a practical three-tank system are utilized to demonstrate the correctness and effectiveness of the proposed estimation scheme.

Fuzzy-Model-Based Lateral Control for Networked Autonomous Vehicle Systems Under Hybrid Cyber-Attacks

This article addresses the problem of lateral control problem for networked-based autonomous vehicle systems. A novel solution is presented for nonlinear autonomous vehicles to smoothly follow the planned path under external disturbances and network-induced issues, such as cyber-attacks, time delays, and limited bandwidths. First, a fuzzy-model-based system is established to represent the nonlinear networked vehicle systems subject to hybrid cyber-attacks. To reduce the network burden and effects of cyber-attacks, an asynchronous resilient event-triggered scheme (ETS) is proposed. A dynamic output-feedback control method is developed to address the underlying problem. Conditions are derived to obtain the output-feedback controller and resilient asynchronous ETS such that the closed-loop switched fuzzy system is globally exponentially stable. Examples are provided to demonstrate the effectiveness and merits of the proposed new control design techniques.

Secure Control of Networked Control Systems Using Dynamic Watermarking

We here investigate the secure control of networked control systems developing a new dynamic watermarking (DW) scheme. First, the weaknesses of the conventional DW scheme are revealed, and the tradeoff between the effectiveness of false data injection attack (FDIA) detection and system performance loss is analyzed. Second, we propose a new DW scheme, and its attack detection capability is interrogated using the additive distortion power of a closed-loop system. Furthermore, the FDIA detection effectiveness of the closed-loop system is analyzed using auto/cross-covariance of the signals, where the positive correlation between the FDIA detection effectiveness and the watermarking intensity is measured. Third, the tolerance capacity of FDIA against the closed-loop system is investigated, and theoretical analysis shows that the system performance can be recovered from FDIA using our new DW scheme. Finally, the experimental results from a networked inverted pendulum system demonstrate the validity of our proposed scheme.

Dynamic Event-Triggered Output Feedback Control for Networked Systems Subject to Multiple Cyber Attacks

This article is concerned with the problem of the H_∞ output feedback control for a class of event-triggered networked systems subject to multiple cyber attacks. Two dynamic event-triggered generators are equipped at sensor and observer sides, respectively, to lower the frequency of unnecessary data transmission. The sensor-to-observer (STO) channel and observer-to-controller (OTC) channel are subject to deception attacks and Denial-of-Service (DoS) attacks, respectively. The aim of the addressed problem is to design an output feedback controller, with the consideration of the effects of dynamic event-triggered schemes (DETSs) and multiple cyber attacks. Sufficient condition is derived, which can guarantee that the resulted closed-loop system is asymptotically mean-square stable (AMSS) with a prescribed H_∞ performance. Moreover, we provide the desired output feedback controller design method. Finally, the effectiveness of the proposed method is demonstrated by an example.