Chinese Journal of Network and Information Security ›› 2018, Vol. 4 ›› Issue (11): 57-68.doi: 10.11959/j.issn.2096-109x.2018086

• Papers • Previous Articles     Next Articles

Intrusion detection model based on non-symmetric convolution auto-encode and support vector machine

Jialin WANG,Jiqiang LIU,Di ZHAO,Yingdi WANG,Yingxiao XIANG,Tong CHEN,Endong TONG,Wenjia NIU   

  1. Beijing Key Laboratory of Security and Privacy in Intelligent Transportation,Beijing Jiaotong University,Beijing 100044,China
  • Revised:2018-10-24 Online:2018-11-15 Published:2019-01-03
  • Supported by:
    The Fundamental Research Funds for the Central Universities(2017RC016);The National Natural Science Foundation of China(61672092);Science and Technology on Information Assurance Laboratory(614200103011711);Beijing Excellent Talent Training Project(BMK2017B02-2);China Scholarship Council(201807095014)


Network intrusion detection system plays an important role in protecting network security.With the continuous development of science and technology,the current intrusion technology cannot cope with the modern complex and volatile network abnormal traffic,without taking into account the scalability,sustainability and training time of the detection technology.Aiming at these problems,a new deep learning method was proposed,which used unsupervised non-symmetric convolutional auto-encoder to learn the characteristics of the data.In addition,a new method based on the combination of non-symmetric convolutional auto-encoder and multi-class support vector machine was proposed.Experiments on the data set of KDD99 show that the method achieves good results,significantly reduces training time compared with other methods,and further improves the network intrusion detection technology.

Key words: intrusion detection technology, convolutional auto-encoder, support vector machine, network security

CLC Number: 

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