基于雾计算的无线传感器网络联合入侵检测算法

为了保障无线传感器网络的安全性，提出一种基于雾计算的联合入侵检测算法Fed-XGB。Fed-XGB算法通过引入雾计算节点扩展网络边缘，减少通信时延，在提升联合学习全局模型和局部模型准确率的同时，降低了传输带宽和隐私泄露风险；通过改进基于直方图的近似计算方法，适应无线传感器网络数据不均衡特征；通过引入TOP-K梯度选择，最小化模型参数上传次数，提高模型参数交互效率。实验结果表明: Fed-XGB算法的检测准确率在0.97以上，误报率在0.036以下，优于其他对比算法；在遭受中毒攻击及数据含噪的情况下，算法检测分类性能依然稳定，具有较强的鲁棒性。 

Fog computing-based federated intrusion detection algorithm for wireless sensor networks

In order to guarantee the security of wireless sensor networks, a federated intrusion detection algorithm Fed-XGB based on fog computing is proposed. The Fed-XGB algorithm extends the edge of the network by introducing fog computing nodes, reduces communication delay, improves the accuracy of joint learning of global and local models, and reduces the transmission bandwidth and the risk of privacy leakage. By improving the approximate calculation method based on the histogram, this algorithm can adapt to the characteristics of unbalanced data in wireless sensor networks. Through the introduction of the TOP-K gradient selection, the number of uploads of model parameters is minimized, and the interaction efficiency of model parameters is improved. Experimental results show that the detection accuracy of the Fed-XGB algorithm is above 0.97, and the false alarm rate is below 0.036, which is better than other comparison algorithms. The results also show that, in the face of poisoning attacks and noisy data, the detection and classification performance are still stable and has strong robustness.