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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| Veröffentlicht in: | Neurocomputing (Amsterdam) Jg. 508; S. 71 - 78 |
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Elsevier B.V
07.10.2022
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Yi, Xiaojian Liu, Shulin Ma, Lifeng Yu, Huiyang Wang, Pengxiang |
| Author_xml | – sequence: 1 givenname: Xiaojian surname: Yi fullname: Yi, Xiaojian organization: School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China – sequence: 2 givenname: Huiyang surname: Yu fullname: Yu, Huiyang organization: School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China – sequence: 3 givenname: Pengxiang surname: Wang fullname: Wang, Pengxiang organization: School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China – sequence: 4 givenname: Shulin surname: Liu fullname: Liu, Shulin organization: Quality and Reliability Center, China Institute of Marine Technology and Economy, Beijing, China – sequence: 5 givenname: Lifeng surname: Ma fullname: Ma, Lifeng organization: School of Automation, Nanjing University of Science and Technology, Nanjing, China |
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| Keywords | Set-membership filtering Encoding–decoding mechanism Cyber-attacks Neural networks |
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