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A Novel Cloud Intrusion Detection System Using Feature Selection and Classification

Author

Listed:
  • Anand Kannan

    (Department of ICT, KTH University, Stockholm, Sweden)

  • Karthik Gururajan Venkatesan

    (Department of ICT, KTH University, Stockholm, Sweden)

  • Alexandra Stagkopoulou

    (Department of ICT, KTH University, Stockholm, Sweden)

  • Sheng Li

    (Department of ICT, KTH University, Stockholm, Sweden)

  • Sathyavakeeswaran Krishnan

    (Department of IT, Uppsala University, Uppsala, Sweden)

  • Arifur Rahman

    (Department of WNE, Linköping University, Linköping, Sweden)

Abstract

This paper proposes a new cloud intrusion detection system for detecting the intruders in a traditional hybrid virtualized, cloud environment. The paper introduces an effective feature selection algorithm called Temporal Constraint based on Feature Selection algorithm and also proposes a classification algorithm called hybrid decision tree. This hybrid decision tree has been developed by extending the Enhanced C4.5 algorithm an existing decision tree based classifier. Furthermore, the experiments conducted on the sample Cloud Intrusion Detection Datasets (CIDD) show that the proposed cloud intrusion detection system provides better detection accuracy than the existing work and reduces the false positive rate.

Suggested Citation

  • Anand Kannan & Karthik Gururajan Venkatesan & Alexandra Stagkopoulou & Sheng Li & Sathyavakeeswaran Krishnan & Arifur Rahman, 2015. "A Novel Cloud Intrusion Detection System Using Feature Selection and Classification," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 11(4), pages 1-15, October.
    • Handle: RePEc:igg:jiit00:v:11:y:2015:i:4:p:1-15
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