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With the widespread application of Multi-Agent Systems (MAS) in complex networks, their robustness and consistency under node failures or network attacks have become critical challenges. This paper proposes a solution combining deep learning-based attack detection and a Local Topology Optimization Algorithm (LTOA) to enhance the stability and security of MAS in complex environments. The improved ResNet-50 model was trained on the CSE-CIC-IDS 2018 dataset, incorporating key enhancements such as the Squeeze-and-Excitation Block (SEBlock), Multi-Head Self-Attention Mechanism (MHSA), Depthwise Separable Convolution, and Bidirectional LSTM (Bi-LSTM). These modifications significantly improved the model’s feature extraction capability and time-series modeling performance. Following attack detection, this paper introduces a topology reconstruction strategy based on LTOA. The proposed method removes failed nodes and prioritizes their replacement with the neighboring nodes of the lowest degree, thereby preserving the original topology structure, minimizing communication overhead, and reducing topology perturbations. Additionally, Kruskal’s algorithm is integrated to construct a Minimum Spanning Tree (MST), ensuring global network connectivity and communication efficiency. Experimental results demonstrate that the proposed method not only achieves high-precision network attack detection but also facilitates rapid topology reconstruction after node failures. This significantly enhances the robustness and consistency control of the system, providing effective technical support for the stable operation of MAS in complex network environments.

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Consensus Control for Network Security in Multi-agent Systems Based on Deep Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Yan Cui,
  • Feng Miao,
  • Ruifeng Liu

摘要

With the widespread application of Multi-Agent Systems (MAS) in complex networks, their robustness and consistency under node failures or network attacks have become critical challenges. This paper proposes a solution combining deep learning-based attack detection and a Local Topology Optimization Algorithm (LTOA) to enhance the stability and security of MAS in complex environments. The improved ResNet-50 model was trained on the CSE-CIC-IDS 2018 dataset, incorporating key enhancements such as the Squeeze-and-Excitation Block (SEBlock), Multi-Head Self-Attention Mechanism (MHSA), Depthwise Separable Convolution, and Bidirectional LSTM (Bi-LSTM). These modifications significantly improved the model’s feature extraction capability and time-series modeling performance. Following attack detection, this paper introduces a topology reconstruction strategy based on LTOA. The proposed method removes failed nodes and prioritizes their replacement with the neighboring nodes of the lowest degree, thereby preserving the original topology structure, minimizing communication overhead, and reducing topology perturbations. Additionally, Kruskal’s algorithm is integrated to construct a Minimum Spanning Tree (MST), ensuring global network connectivity and communication efficiency. Experimental results demonstrate that the proposed method not only achieves high-precision network attack detection but also facilitates rapid topology reconstruction after node failures. This significantly enhances the robustness and consistency control of the system, providing effective technical support for the stable operation of MAS in complex network environments.