Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (9): 2604-2610.DOI: 10.11772/j.issn.1001-9081.2019020327

• Cyber security • Previous Articles     Next Articles

Network intrusion detection model based on improved convolutional neural network

YANG Hongyu, WANG Fengyan   

  1. College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China
  • Received:2019-02-27 Revised:2019-04-02 Online:2019-09-10 Published:2019-05-14
  • Supported by:

    This work is partially supported by the Civil Aviation Joint Research Fund Project of National Natural Science Foundation of China (U1833107).


杨宏宇, 王峰岩   

  1. 中国民航大学 计算机科学与技术学院, 天津 300300
  • 通讯作者: 杨宏宇
  • 作者简介:杨宏宇(1969-),男,吉林长春人,教授,博士,CCF会员,主要研究方向:网络信息安全;王峰岩(1993-),男,河南南阳人,硕士研究生,主要研究方向:网络信息安全。
  • 基金资助:



Aiming at the problems of deep learning based network intrusion detection technology such as low detection efficiency, easy over-fitting and weak generalization ability of model training, an Improved Convolutional Neural Network (ICNN) based Intrusion Detection Model (IBIDM) was proposed. Different from the traditional "convolution-pooling-full connection" cascading network design method, the model adopted the design method of cross-layer aggregation network. Firstly, the pre-processed training set data was forwardly propagated as input data and the network features were extracted, and the merge operation was performed on the output data of the cross-layer aggregation network. Then, the training error was calculated according to the classification result and the model was iteratively optimized to convergence by the back propagation process. Finally, a classification test experiment was performed on the test dataset using the trained classifier. The experimental results show that IBIDM has high intrusion detection accuracy and true positive rate, and its false positive rate is low.

Key words: network intrusion detection, convolutional neural network, forward propagation, cross-layer aggregation, iterative optimization



关键词: 网络入侵检测, 卷积神经网络, 前向传播, 跨层聚合, 迭代优化

CLC Number: