Stabilizing reaction–diffusion neural networks: Overcoming Denial-of-Service attacks with state-dependent impulsive control strategies

Lulu Li, Rongzhi Li, Yang Liu and Lei Wang

Mathematics and Computers in Simulation (MATCOM), 2026, vol. 248, issue C, 765-780

Abstract: In this paper, we explore the global exponential stability of reaction–diffusion neural networks (RDNNs) in the presence of Denial-of-Service (DoS) attacks and state-dependent impulsive control (SDIC). Utilizing the B-equivalence method, we transform the state-dependent impulsive system into a time-dependent equivalent, thereby maintaining its stability properties. By applying a comparison theorem alongside specific assumptions regarding the nature of DoS attacks, we derive sufficient conditions that guarantee the global exponential stability of RDNNs. To illustrate and validate our theoretical results, we provide numerical examples that demonstrate the effectiveness of our proposed conditions.

Keywords: State-dependent impulsive control; Reaction–diffusion neural networks; DoS attacks; B-equivalence (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:248:y:2026:i:c:p:765-780

DOI: 10.1016/j.matcom.2026.04.042

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