International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Intrusion Detection using Attribute Subset Selector Bagging (ASUB) to Handle Imbalance and Noise

  • Priya, A.Sagaya (Department of Computer Science, St. Joseph's College (Autonomous), Affiliated to Bharathidasan University) ;
  • Kumar, S.Britto Ramesh (Department of Computer Science, St. Joseph's College (Autonomous), Affiliated to Bharathidasan University)
  • Received : 2022.05.05
  • Published : 2022.05.30
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Abstract

Network intrusion detection is becoming an increasing necessity for both organizations and individuals alike. Detecting intrusions is one of the major components that aims to prevent information compromise. Automated systems have been put to use due to the voluminous nature of the domain. The major challenge for automated models is the noise and data imbalance components contained in the network transactions. This work proposes an ensemble model, Attribute Subset Selector Bagging (ASUB) that can be used to effectively handle noise and data imbalance. The proposed model performs attribute subset based bag creation, leading to reduction of the influence of the noise factor. The constructed bagging model is heterogeneous in nature, hence leading to effective imbalance handling. Experiments were conducted on the standard intrusion detection datasets KDD CUP 99, Koyoto 2006 and NSL KDD. Results show effective performances, showing the high performance of the model.

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References

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