Resilient Consensus with Mobile Detectors Against Malicious Attacks

Zhao, Chengcheng; He, Jianping; Chen, Jiming

doi:10.1109/tsipn.2017.2742859

Cited by 64 publications

(37 citation statements)

References 39 publications

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“…A malicious agent is misbehaving due to, for example, stubborn traits, and it sends information in a broadcast manner [8], [16]. Byzantine agents on the other hand are often thought of as one of the most dangerous attackers [18], [23], [25], who typically possess a thorough intelligence of the entire network and hence can potentially collude with other Byzantine agents to manipulate the network by sending wrong information in a point-to-point manner. By the above definitions, both malicious and Byzantine agents can renew their opinions in an arbitrary way at every time step, and hence all malicious agents turn out to be Byzantine, but not vice versa.…”

Section: B Opinion Modelmentioning

confidence: 99%

“…Resilient learning-based protocols are introduced in [24] to find optimal solutions to consensus problems in the presence of malicious attackers and uncertainties in system. By utilizing mobile detectors, it is shown in [25] that consensus can be maintained against Byzantine agents whose number is not restrained by the network connectedness. However, to our knowledge, only single state value is assumed in the prior works, which are thus unable to capture individuals in social networks who have private opinions deviating from the opinions they express.…”

Section: Introductionmentioning

confidence: 99%

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Resilient Consensus for Expressed and Private Opinions

Shang

2021

IEEE Trans. Cybern.

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Section: B Opinion Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Resilient Consensus for Expressed and Private Opinions

Shang

2021

IEEE Trans. Cybern.

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“…In stationary scenarios, the convergence state is independent of time, that is, X 0 i = X 0 i−1 = X 0 . Therefore, equation (25) turns to:…”

Section: ) Stationary State Estimationmentioning

confidence: 99%

“…Assume the convergence point X 0 i is a combination of a time-independent value and a time-dependent value, such that X 0 i = X 0 + ρ i . After taking original values into (25), we get (27) into the time-independent and timedependent components to get…”

mentioning

confidence: 99%

Resilient Distributed Diffusion in Networks With Adversaries

Abbas

Koutsoukos

2020

IEEE Trans. on Signal and Inf. Process. over Networks

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In this paper, we study resilient distributed diffusion for multi-task estimation in the presence of adversaries where networked agents must estimate distinct but correlated states of interest by processing streaming data. We show that in general diffusion strategies are not resilient to malicious agents that do not adhere to the diffusion-based information processing rules. In particular, by exploiting the adaptive weights used for diffusing information, we develop time-dependent attack models that drive normal agents to converge to states selected by the attacker. We show that an attacker that has complete knowledge of the system can always drive its targeted agents to its desired estimates. Moreover, an attacker that does not have complete knowledge of the system including streaming data of targeted agents or the parameters they use in diffusion algorithms, can still be successful in deploying an attack by approximating the needed information. The attack models can be used for both stationary and non-stationary state estimation. In addition, we present and analyze a resilient distributed diffusion algorithm that is resilient to any data falsification attack in which the number of compromised agents in the local neighborhood of a normal agent is bounded. The proposed algorithm guarantees that all normal agents converge to their true target states if appropriate parameters are selected. We also analyze trade-off between the resilience of distributed diffusion and its performance in terms of steady-state mean-square-deviation (MSD) from the correct estimates. Finally, we evaluate the proposed attack models and resilient distributed diffusion algorithm using stationary and nonstationary multi-target localization.

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“…For example, in [25], [30], detection techniques using unknown input observers are proposed, in which case the initial values of all normal nodes can be found though they require the global knowledge of the network topology. Methods based on more local information can be found in, e.g., [1], [8], [37]. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Resilient Consensus Through Event-Based Communication

Wang

Ishii

2020

IEEE Trans. Control Netw. Syst.

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We consider resilient versions of discrete-time multiagent consensus in the presence of faulty or even malicious agents in the network. In particular, we develop event-triggered update rules which can mitigate the influence of the malicious agents and at the same time reduce the communication. Each regular agent updates its state based on a given rule using its neighbors' information. Only when the triggering condition is satisfied, the regular agents send their current states to their neighbors. Otherwise, the neighbors will continue to use the state received the last time. Assuming that a bound on the number of malicious nodes is known, we propose two update rules with event-triggered communication. They follow the socalled mean subsequence reduced (MSR) type algorithms and ignore values received from potentially malicious neighbors. We characterize the necessary connectivity in the network for the algorithms to perform correctly, which are stated in terms of the notion of graph robustness. A numerical example is provided to demonstrate the effectiveness of the proposed approach.

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Resilient Consensus with Mobile Detectors Against Malicious Attacks

Cited by 64 publications

References 39 publications

Resilient Consensus for Expressed and Private Opinions

Resilient Consensus for Expressed and Private Opinions

Resilient Distributed Diffusion in Networks With Adversaries

Resilient Consensus Through Event-Based Communication

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