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Intrusion detection model with twin support vector machines

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Abstract

Intrusion detection system (IDS) is becoming a critical component of network security. However, the performance of many proposed intelligent intrusion detection models is still not competent to be applied to real network security. This paper aims to explore a novel and effective approach to significantly improve the performance of IDS. An intrusion detection model with twin support vector machines (TWSVMs) is proposed. In this model, an efficient algorithm is also proposed to determine the parameter of TWSVMs. The performance of the proposed intrusion detection model is evaluated with KDD’99 dataset and is compared with those of some recent intrusion detection models. The results demonstrate that the proposed intrusion detection model achieves remarkable improvement in intrusion detection rate and more balanced performance on each type of attacks. Moreover, TWSVMs consume much less training time than standard support vector machines (SVMs).

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Authors and Affiliations

  1. Information Security Center; National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Jun He  (何 俊) & Shi-hui Zheng  (郑世慧)

Authors
  1. Jun He  (何 俊)
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  2. Shi-hui Zheng  (郑世慧)
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Corresponding author

Correspondence to Jun He  (何 俊).

Additional information

Foundation item: the National Natural Science Foundation of China (Nos. 61202082 and 61003285), and the Fundamental Research Funds for the Central Universities of China (Nos. BUPT2012RC0219 and BUPT2012RC0218)

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He, J., Zheng, Sh. Intrusion detection model with twin support vector machines. J. Shanghai Jiaotong Univ. (Sci.) 19, 448–454 (2014). https://doi.org/10.1007/s12204-014-1524-4

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  • DOI: https://doi.org/10.1007/s12204-014-1524-4

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