Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11): 293-301.doi: 10.11896/jsjkx.210800252

• Computer Network • Previous Articles     Next Articles

Application Layer Protocol Recognition Based on Residual Network and Recurrent Neural Network

WU Ji-sheng, HONG Zheng, MA Tian-tian, LIN Pei-hong   

  1. Command and Control Engineering College,Army Engineering University of PLA,Nanjing 210000,China
  • Received:2021-08-28 Revised:2021-12-07 Online:2022-11-15 Published:2022-11-03
  • About author:WU Ji-sheng,born in 1997,postgra-duate.His main research interests include cyberspace security and protocol recognition.
    HONG Zheng,born in 1979,Ph.D,associate professor.His main researchintere-sts include cyberspace security and protocol reverse engineering.
  • Supported by:
    National Key R & D Program of China(2017YFB0802900).

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Abstract: Existing protocol recognition methods cannot effectively extract the temporal and spatial characteristics of protocol data,which leads to low accuracy of protocol recognition.An application layer protocol recognition method based on one dimensional residual network and recurrent neural network is proposed.The proposed model consists of one dimensional preactivated residual network(PreResNet) and bidirectional gated recurrent neural network(BiGRU).The PreResNet is used to extract spatial characteristics of the protocol data,and the BiGRU is used to extract temporal characteristics of the protocol data.The attention mechanism is used to extract the key features related to protocol recognition to improve the accuracy of protocol recognition.The proposed method firstly extracts the application layer protocol data from network traffic,and the data is preprocessed and transformed into one dimensional vectors.Then the classification model is trained with the training data and a mature protocol recognition model is obtained.Finally,the trained classification model is used to identify the application layer protocols.Experimental results on public dataset ISCX2012 show that the proposed protocol recognition model has an overall accuracy of 96.87% and an average F value of 96.81%,which are higher than those of other protocol recognition models.

Key words: Recurrent neural network, Residual network, Protocol recognition, Network security

CLC Number: 

  • TP398.08
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