With the wide applications of industrial wireless network technologies, the industrial control system (ICS) is evolving from wired and centralized to wireless and distributed, during which eavesdropping and attacking become serious problems. To guarantee the security of wireless and distributed ICS, this article establishes an end-edge collaborative lightweight secure federated learning (LSFL) architecture and proposes an LSFL anomaly detection strategy. Specifically, we first design a residual multihead self-attention convolutional neural network for local feature learning, where the variability and dependence of spatial-temporal features can be sufficiently evaluated. Then, to reduce the wireless communication cost for parameter exchange and edge federal learning, we propose a dynamic parameter pruning algorithm by evaluating the contribution of each parameter based on the information entropy gain. Furthermore, to ensure the parameter security during wireless transmission in the open radio environment, we propose an adaptive key generation algorithm for parameter encryption. Finally, the proposed strategy is experimentally validated on representative datasets, including Smart Meter, NSL-KDD, and UNSW-NB15. Experimental results demonstrate that the proposed strategy achieves 99% accuracy on different datasets, where at least 89.6% wireless communication cost is reduced and tampering/injecting attacks are defended.

中文翻译：

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用于无线工业控制系统异常检测的终端边缘协作轻量级安全联合学习

随着工业无线网络技术的广泛应用，工业控制系统（ICS）正从有线、集中式向无线、分布式演进，窃听和攻击成为严重问题。为了保证无线分布式ICS的安全，本文建立了一种端边协同轻量级安全联邦学习（LSFL）架构，并提出了一种LSFL异常检测策略。具体来说，我们首先设计了一个用于局部特征学习的残差多头自注意力卷积神经网络，可以充分评估时空特征的可变性和依赖性。然后，为了降低参数交换和边缘联邦学习的无线通信成本，我们通过基于信息熵增益评估每个参数的贡献，提出了一种动态参数剪枝算法。此外，为了确保开放无线电环境下无线传输过程中的参数安全，我们提出了一种用于参数加密的自适应密钥生成算法。最后，所提出的策略在代表性数据集（包括 Smart Meter、NSL-KDD 和 UNSW-NB15）上进行了实验验证。实验结果表明，该策略在不同数据集上实现了 99% 的准确率，至少降低了 89.6% 的无线通信成本，并防御了篡改/注入攻击。