Netinfo Security ›› 2020, Vol. 20 ›› Issue (10): 67-74.doi: 10.3969/j.issn.1671-1122.2020.10.009

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Classification of Malicious Network Traffic Based on Improved Bilinear Convolutional Neural Network

GU Zhaojun1,2, HAO Jintao1,2(), ZHOU Jingxian1   

  1. 1. Information Security Evaluation Center, Civil Aviation University of China, Tianjin 300300, China
    2. Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China
  • Received:2020-03-02 Online:2020-10-10 Published:2020-11-25
  • Contact: HAO Jintao E-mail:haojintao291@163.com

Abstract:

In this paper, improved bilinear convolutional neural network(IBCNN) is proposed for malicious network traffic classification. Different from traditional unilinear network structure, the network adopts the design idea of cross-layer multi-feature fusion. Firstly, use two neural networks(network A, network B) based on VGG-Net for feature extraction, connect cross-layer multi-feature fusion modules for feature fusion to improve feature expression ability. Then, optimization through multiple iterations, train the network model to fit state. Finally, network model completed by training using test set, get classification results. The experimental verification and evaluation index calculation show that this algorithm has higher accuracy, precision and F value in the classification of malicious network traffic.

Key words: cyber security, network traffic classification, convolutional neural network, feature fusion, iterative optimization

CLC Number: