Research on abnormal network traffic detection method based on machine learning

Xiaobo Tan; Yumei Jia

doi:10.1117/12.2675157

25 May 2023 Research on abnormal network traffic detection method based on machine learning

Xiaobo Tan, Yumei Jia

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 1263609 (2023) https://doi.org/10.1117/12.2675157
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Aiming at the problem of high false detection rate and low detection rate of abnormal network traffic detection methods due to uneven data distribution of current network traffic datasets, a network abnormal traffic detection method based on CNN-GRU is proposed. The method firstly performs local feature learning on the data according to the local learning ability unique to the convolutional neural network (CNN), and replaces the fully connected layer with a global average pooling layer to reduce the feature dimension and parameters; secondly, it uses a gated recurrent unit (GRU) learns the time series features of the data to improve the nonlinear representation ability of the method; finally, for the imbalance between the data, the cross entropy loss function is optimized, and the penalty for abnormal class sample detection and normal class sample detection is passed. The adjustment of the weight increases the attention of the model to the abnormal samples, thereby improving the accuracy of the model detection. The simulation results show that the method can effectively improve the classification and detection performance.

Citation Download Citation

Xiaobo Tan and Yumei Jia "Research on abnormal network traffic detection method based on machine learning", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 1263609 (25 May 2023); https://doi.org/10.1117/12.2675157

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