Frequency domain analysis of resilient consensus in multi-agent systems subject to an integrity attack

Ahmed, Zahoor; Saeed, Muhammad Abid; Jenabzadeh, Ahmadreza; Zhang, Guoqing

doi:10.1016/j.isatra.2020.11.014

Cited by 19 publications

(12 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An algorithm is discussed for the detection of deception attack in an application which could be remote state application, smart sensors used for data receiving [105]. Second application of such kind has been studied for the detection of bias injection attacks for stochastic rectilinear dynamical scheme [12,[106][107][108][109]. Multiclass support vector machine was discussed for building an intrusion detection model.…”

Section: Q 2 Detector Kalman Filtersmentioning

confidence: 99%

An Overview of Recent Advances of Resilient Consensus for Multiagent Systems under Attacks

Aslam

Ahmed

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

Consensus control of multiagent systems (MASs) has been one of the most extensive research topics in the field of robotics and automation. The information sharing among the agents in the MASs depends upon the communication network because the interaction of agents may affect the consensus performance of the agents in a communication network. An unexpected fault and attack may occur on one agent and can propagate through the communication network into other agents. Thus, this may cause severe degradation of the whole MASs. In this paper, we first discussed MAS technologies. After that available technologies for the modeling of attacks and fundamental issues due to attacks on MAS attacks were discussed. We also introduced cooperative attack methodologies and model-based attack methodology. Objective of this article is to provide comprehensive study on recent advances in consensus control of MASs under attacks covering the published results until 2021. This survey presents different kinds of attacks, their estimation and detection, and resilient control against attacks. At the end, the survey accomplishes some potential recommendations for future direction to solve the key issues and challenges reported for secure consensus control of MASs.

show abstract

Section: Q 2 Detector Kalman Filtersmentioning

confidence: 99%

An Overview of Recent Advances of Resilient Consensus for Multiagent Systems under Attacks

Aslam

Ahmed

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is important to say that the purpose of SU is to understand a strategy π for the selection of its action aðrÞ which is constructed on the present state mðrÞ in a method that discounted cumulative reward reduced maximum as studied in [38]. In Equation (10), "Ɣ" is the discount factor and T −1 is representing the time frame, at which the targeted state is touched.…”

Section: An Overview Of Crnmentioning

confidence: 99%

A Deep Learning-Based Power Control and Consensus Performance of Spectrum Sharing in the CR Network

Aslam

Ahmed

et al. 2021

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

The cognitive radio network (CRN) is aimed at strengthening the system through learning and adjusting by observing and measuring the available resources. Due to spectrum sensing capability in CRN, it should be feasible and fast. The capability to observe and reconfigure is the key feature of CRN, while current machine learning techniques work great when incorporated with system adaption algorithms. This paper describes the consensus performance and power control of spectrum sharing in CRN. (1) CRN users are considered noncooperative users such that the power control policy of a primary user (PU) is predefined keeping the secondary user (SU) unaware of PU’s power control policy. For a more efficient spectrum sharing performance, a deep learning power control strategy has been developed. This algorithm is based on the received signal strength at CRN nodes. (2) An agent-based approach is introduced for the CR user’s consensus performance. (3) All agents reached their steady-state value after nearly 100 seconds. However, the settling time is large. Sensing delay of 0.4 second inside whole operation is identical. The assumed method is enough for the representation of large-scale sensing delay in the CR network.

show abstract

“…Li et al [29] designed an ETC for discrete time-varying MASs with DIA. Ahmed et al [30] proposed a frequency domain analysis method for MASs against DIA. Dong et al [31] developed a resilient consensus method for MASs under external and malicious DIA.…”

Section: Introductionmentioning

confidence: 99%

Distributed Event-Triggered Bipartite Consensus for Multiagent Systems Against Injection Attacks

Zhao

Shan

et al. 2023

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

This paper studies fully distributed data-driven problems for heterogeneous nonlinear discrete-time multiagent systems (MASs) with fixed and switching topologies preventing injection attacks. We first develop an enhanced compact form dynamic linearization model by applying the designed distributed bipartite combined measurement error function of the MASs,. we then an event-triggered control machination is developed and, a fully distributed event-triggered bipartite consensus framework is designed, where the dynamics information of MASs is no longer needed. Meanwhile, the restriction of topology is further relieved, which is fitting leader-less, leader-follower, and even containment control. Moreover, to prevent injection attacks, nervous network-based detection and compensation schemes are developed. Rigorous convergence proof is presented that the bipartite consensus error is ultimately boundedness by utilizing the Lyapunov stability theory. Finally, the correctness and effectiveness of the designed method are verified by simulation and hardware experiments.

show abstract

Frequency domain analysis of resilient consensus in multi-agent systems subject to an integrity attack

Cited by 19 publications

References 28 publications

An Overview of Recent Advances of Resilient Consensus for Multiagent Systems under Attacks

An Overview of Recent Advances of Resilient Consensus for Multiagent Systems under Attacks

A Deep Learning-Based Power Control and Consensus Performance of Spectrum Sharing in the CR Network

Distributed Event-Triggered Bipartite Consensus for Multiagent Systems Against Injection Attacks

Contact Info

Product

Resources

About