Research on anomalous behavior detection of federated deep learning network intrusion based on FATE-CNN
Hongbin Zhu, Xiao Peng, Xianzhou Gao · 2023
In recent years, with the rapid development of China's network scale, the frequency of power Internet has also increased sharply. But at the same time, in the process of power grid intelligent terminal access, all kinds of abnormal network attacks are increasingly frequent. As a key link in the power network security protection, the detection of network abnormal intrusion behavior has been paid more and more attention by researchers in recent years. In view of this, this paper proposes a network anomalous intrusion behavior detection method based on FATE-CNN. It corresponds several local intrusion detection datasets to federated learning devices one by one, and uses a dynamic local iteration method to gradually obtain the best global model. Through the comparative experiment with four intrusion detection models of LIBSVM, CNN, DNN and DBN-EGWO-K-KELM, it is verified that the model algorithm can effectively improve the value of abnormal network intrusion behavior of NSL-KDD, UNSW-NB 15 and CICIDS2017, and improve the network security of the intelligent terminal of the power grid.