Fixed-time projective quasi-synchronization for multi-layer coupled memristive neural networks under deception attacks

Sampled-data control for mean-square exponential stabilization of memristive neural networks under deception attacks

This paper explores the security control problem of a class of hyperbolic partial differential equation (PDE) systems described by a set of nonlinear ordinary differential equation (ODE) under deception attacks. Compared with the control design process of traditional single ODE systems, the strong coupling characteristics of partial differential equation-ordinary differential equation (PDE-ODE) systems make the control design under deception attacks more difficult. By applying the infinite-dimensional backstepping transformation and its inverse transformation, the original PDE subsystem is transformed into a more tractable target system, thus effectively achieving control performance. The neural network approximation algorithm is adopted to separate the coupling effects caused by attacks, focusing on addressing the unknown nonlinear dynamics of the system. Deception attacks are modeled as time-varying weights with unknown control directions, and the Nussbaum function is employed to address the problem of unknown control directions. In addition, a dynamic event-triggered mechanism with a novel switching threshold is designed to alleviate the communication burden. The proposed control algorithm ensures that both the closed-loop system states and the actuator states are bounded. Finally, this conclusion is verified through numerical simulation.

Indirect adaptive neural tracking control of USVs under injection and deception attacks

In this paper, the finite‐time adaptive resilient control problem for a class of switched nonlinear systems with deception attacks by using the dynamic surface control (DSC) strategy is addressed. A more general class of uncertain nonstrict‐feedback switched nonlinear systems with the sensor and actuator deception attacks are considered. Deception attacks can damage sensor, control and switching data, which results in the switching signal and the finite‐time control gains unknown, meanwhile the conventional dynamic surface errors inacessible. To overcome the difficulty, first, the approximation technique of neural networks (NNs) is used to deal with the unknown nonlinear terms and compensate the sensor and actuator deception attacks. Secondly, a new coordinate transformation and a novel switched adaptive finite‐time filter are designed to mitigate the effects caused by the sensor attacks and avoid the problem of “explosion of complexity”. Thirdly, Nussbaum technology is exploited to handle the unknown finite‐time control gains problem caused by deception attacks. Then, by the proposed control strategy, the practical finite‐time stability analysis is presented. Finally, a simulation example is provided to validate the feasibility of the proposed control method.

ABSTRACTAt this job, the adaptive control problem is investigated for a class of non‐linear cyber‐physical systems (CPSs), where the CPSs considered are not only subject to deception attacks and time delay, but also contain uncertain input powers. The deception attacks result in the actual values of the system state being unavailable and control gains being unknown. On the basis of the theory of Lyapunov stability, a new adaptive neural‐networks‐based control scheme is designed to guarantee the stability of the closed‐loop system and mitigate the impact of deception attacks. Compared with the existing works in literature, (1) the input powers of the CPSs considered in this article are unknown and new controllers are constructed based on the neural network approximation technique; (2) the influence of unknown time delay is eliminated by using a novel Lyapunov–Krasovskii function. Furthermore, in order to address unknown gains caused by deception attacks, the Nussbaum gain technique is firstly extended to the CPSs with power uncertainties. Finally, the simulation results confirm the effectiveness of the control strategy presented in this work.

In this article, the chance-constrained 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 estimator such that the prefixed probabilistic constraints of the system error dynamics are satisfied and the 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.

This paper focuses on the design of event-triggered controllers for the synchronization of delayed Takagi–Sugeno (T–S) fuzzy neural networks (NNs) under deception attacks. The traditional event-triggered mechanism (ETM) determines the next trigger based on the current sample, resulting in network congestion. Furthermore, such methods suffer from the issues of deception attacks and unmeasurable system states. To enhance the system stability, we adaptively detect the occurrence of events over a period of time. In addition, deception attacks are recharacterized to describe general scenarios. Specifically, the following enhancements are implemented: First, we use a Bernoulli process to model the occurrence of deception attacks, which can describe a variety of attack scenarios as a type of general Markov process. Second, we introduce a sum-based dynamic discrete event-triggered mechanism (SDDETM), which uses a combination of past sampled measurements and internal dynamic variables to determine subsequent triggering events. Finally, we incorporate a dynamic output feedback controller (DOFC) to ensure the system stability. The concurrent design of the DOFC and SDDETM parameters is achieved through the application of the cone complement linearization (CCL) algorithm. We further perform two simulation examples to validate the effectiveness of the algorithm.

NNs-observer-based fully distributed consensus control for MASs under deception attacks

This article studies the input-to-state stability (ISS) problem of networked control systems (NCSs) subject to both Denial-of-Service (DoS) and unknown deception attacks (DAs). A neural network (NN)-based resilient event-triggered control protocol (RETCP) is first presented to mitigate resource constraints and the adverse effects of cyber attacks, where the NN technology is leveraged to neutralize and approximate the malicious data injected by unknown DAs. Then, we develop a new predictor to compensate for lost signals of NCSs during the DoS threats, so that the NCSs can further tolerate more unfavorable DoS and unknown DAs. It is shown that the resulting NCSs with the designed novel NN-based controller can achieve ISS under the complex attacks. Finally, experimental evaluations are conducted for an uncrewed ground vehicle (UGV) to verify efficacy of the proposed intelligent control protocols.

Adaptive event-triggered extended dissipative synchronization of delayed reaction–diffusion neural networks under deception attacks

This article investigates the control problem of unmanned surface vessels with sensor measurement sensitivity under deception attacks, and proposes a novel self‐triggered adaptive neural control scheme under the backstepping design framework. To solve the control design problem of unknown time‐varying gains caused by deception attacks and measurement sensitivity in kinematic and kinetic channels, the parameter adaptive and neural network technology are involved. In addition, to decrease actuator wear caused by the high‐frequency wave and sensor measurement sensitivity and reduce the computational burden caused by continuous monitoring of the triggered condition, a self‐triggered mechanism is constructed in the controller–actuator channel. Finally, a self‐triggered adaptive neural control solution is proposed, which can guarantee that all signals in the whole closed‐loop system are bounded by theoretical analysis. The effectiveness and superiority are verified by numerical simulations.

In this article, an adaptive predefined accuracy control issue is addressed for vehicle platoon control system (VPCS) with time delay and deception attacks. Two nonnegative switched functions are employed to handle the predefined accuracy control problem. Some radial basis function neural networks (RBF NNs) are applied to estimate uncertain functions. Different from the existed reports on the VPCS, by combining with the switched functions and the Nussbaum functions, an adaptive distributed controller is designed for the VPCS to overcome the influence of time delay and deception attacks so that the spacing errors gather to a preset vale. At last, some simulation results are used to test the correctness of the proposed scheme with predefined accuracy value 0.2. In order to emphasize that the proposed scheme is independent of parameters, we also get a simulation result with an accuracy of 0.1. And some simulation results are also obtained under the strength of time-delays and deception attacks to highlight the valid of the presented method.

This paper focuses on the issue of adaptive neural control for discrete-time switched systems with time delay and deception attacks. Firstly, the switching signal is constrained by the dwell time. Considering that the deception attacks are unknown, the neural network technique is employed to approximate the attack signals. Then, an adaptive state feedback controller is established to compensate for the adverse effects of deception attacks for switched systems. Meanwhile, sufficient conditions for the boundedness of the switched system are given through the Lyapunov functional method, and the controller gains can be obtained by resolving the linear matrix inequality. Finally, the feasibility of the proposed method is illustrated via a numerical example.

In this paper, event-triggered control for uncertain delayed neural networks (DNNs) with actuator saturation against deception attack is discussed. We propose a novel framework into which an event-triggered mechanism (ETM), actuator saturation, system uncertainty, and deception attack are combined. In the framework, the discrete ETM is employed to determine whether the sampled signal should be transmitted to controller so as to save network bandwidth, a random occurrence deception attack model satisfying Bernoulli distribution is introduced to construct the system robust, and actuator saturation is considered due to the actual complex network environment. Based on the framework, we discussed the stochastic stability of a novel delayed neural network model in a closed-loop system. By resorting to the appropriate Lyapunov–Krasovskii functional (LKF), we derive some new sufficient conditions for the stochastically stable of the system and obtain the gain of the system controller using efficient linear matrix inequality (LMI) method. Finally, a numerical example, in the end, demonstrates the effectiveness of the proposed method.

This article investigates the security control issue of delayed coupled fuzzy inertial neural networks (FINNs) under deception attacks. Aiming to alleviate the influence of deception attacks, a fuzzy sampling data security controller is designed. A theoretical structure is formulated to analyze the behavior of the closed-loop system under deceptive interference. On this basis, by constructing a suitable set of Lyapunov functionals (LKFs) and employing inequality techniques, criteria guaranteeing exponential synchronization are established using linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed method is demonstrated via numerical simulations and encryption and decryption analysis. Results show that, affected by deception attacks, the coupling FINNs can achieve exponential synchronization through our developed security control approach.