| Citation: | ZHU Mengyuan, CHEN Zhuo, LIU Pengfei, et al. Fog computing-based federated intrusion detection algorithm for wireless sensor networks[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(10): 1943-1950. doi: 10.13700/j.bh.1001-5965.2021.0766(in Chinese)
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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.
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