Distributed event‐triggered tracking consensus control for privacy preservation in multi‐agent systems with denial‐of‐service attacks

Abstract

This article investigates the distributed tracking consensus control problem based on event-triggered strategy for privacy preservation in nonlinear multi-agent systems which suffer from denial-of-service (DoS) attacks. To address the insecurity of information exposure in the distributed control strategy, the method of privacy preservation for initial information is developed by relying on output maps as masks. Due to the existence of nonlinear dynamics, the neural networks are introduced to deal with unknown nonlinear functions. Then, an event-triggered communication strategy based on the exponential time-varying vector is provided to regulate the information transmission under the situation of DoS attacks. Subsequently, combining the proposed event-triggered strategy, an asynchronous update control scheme is structured to reduce the effect of DoS attacks. It is demonstrated that the tracking consensus can be achieved by utilizing the Lyapunov function and the induction method. Finally, the feasibility of the proposed control method is demonstrated by a simulation example.

State saturated recursive filtering for nonlinear complex networks with energy harvesting sensors and false data injection attacks

“…[13][14][15] Along with science and technology development of network, the issue of security has attracted widespread attention because of the openness and sharing of the network. Several kinds of common network attacks have been extensively studied, for instance denial of service, [16][17][18][19][20][21] replay, 22 deception attacks [23][24][25][26][27][28] and so forth. Thereinto, the most generic attack are the deception attacks, especially false data injection (FDI) attacks.…”

Adaptive containment control of nonlinear multi‐agent systems about privacy preservation with multiple attacks

“…However, it is easy to find from above mentioned researches that there is still a lack of the research for nonlinear systems in the presence of multiple attacks. Therefore, inspired by neural network papers such as References 34–36, how to develop a control method for nonlinear MASs with active leaders to resist the DoS attacks and false data‐injection attacks by neural network is an open and interesting problem to be solved, which is the focus of this paper.…”