Encoding–decoding-based secure filtering for neural networks under mixed attacks

This paper is concerned with the set-membership filtering issue for a class of artificial neural networks subject to mixed attacks. The encoding–decoding communication mechanism is adopted in the processing of data sharing between neurons. During the information exchanges among neurons, both injecti...

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Vydáno v:Neurocomputing (Amsterdam) Ročník 508; s. 71 - 78
Hlavní autoři: Yi, Xiaojian, Yu, Huiyang, Wang, Pengxiang, Liu, Shulin, Ma, Lifeng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 07.10.2022
Témata:
Cyber-attacks
Encoding–decoding mechanism
Neural networks
Set-membership filtering
Set-membership filtering
Encoding–decoding mechanism
Cyber-attacks
Neural networks
ISSN:0925-2312, 1872-8286
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Shrnutí:This paper is concerned with the set-membership filtering issue for a class of artificial neural networks subject to mixed attacks. The encoding–decoding communication mechanism is adopted in the processing of data sharing between neurons. During the information exchanges among neurons, both injection and DoS attacks are concurrently considered to reflect the practical operating conditions of the investigated neural networks. The purpose of the addressed problem is to present an algorithm to estimate the neurons’ states in the presence of mixed attacks, while guaranteeing the estimation errors at each neuron are confined within certain prescribed ellipsoidal region. Sufficient conditions are derived, in terms of convex optimization approach, to ensure the existence of desired filter, and the explicit filtering parameters are obtained via solving the provided set of matrix inequalities. Finally, a numerical simulation example is proposed to show the validity of the obtained theoretical results.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2022.08.041