Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 230300142-8.doi: 10.11896/jsjkx.230300142

• Information Security • Previous Articles     Next Articles

Study on Intrusion Detection Algorithm Based on TCN-BiLSTM

BAI Wanrong1, WEI Feng1, ZHENG Guangyuan2, WANG Baohui2   

  1. 1 State Grid Gansu Electric Power Research Institute,Lanzhou 730070,China
    2 School of Software,Beihang University,Beijing 100191,China
  • Published:2023-11-09
  • About author:BAI Wanrong,born in 1985,postgra-duate,senior engineer,is a member of China Computer Federation.His main research interests include network security and machine learning.
    ZHENG Guangyuan,born in 1996,M.S.His main research interests include artificial intelligence and network security.
  • Supported by:
    Research Project of Multi-source Network Threat Tracing Technology Based on Post-protection(52272222001B).

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Abstract: Network security is directly related to national security.How to accurately and efficiently detect network threats in the power grid is very important.Aiming at the problems of small receptive field and no consideration of data timing characteristics of traditional CNN,combined with spatial and temporal characteristics of network traffic data,an attention intrusion detection algorithm based on time convolution network(TCN) and BiLSTM is proposed.First,feature coding is performed on network traffic characteristics.Then the forest optimization feature screening algorithm is used to reduce the redundancy of the data,and then resampling is carried out to solve the problem of data imbalance.Finally,the data is input into the deep neural network,and the processed data is extracted by the TCN and BiLSTM networks for feature learning.The self-attention mechanism is used for weight allocation,and finally the classification is carried out to realize the intrusion detection.The data set adopts NSL-KDD,and the experimental results show that the algorithm can identify network intrusion detection effectively.

Key words: Intrusion detection, Temporal convolutional network, Bi-directional long short-term memory

CLC Number: 

  • TN915.08
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