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Cluster Computing

, Volume 21, Issue 1, pp 667–680 | Cite as

A hybrid technique using binary particle swarm optimization and decision tree pruning for network intrusion detection

  • Arif Jamal Malik
  • Farrukh Aslam KhanEmail author
Article
  • 237 Downloads

Abstract

A major drawback of signature-based intrusion detection systems is the inability to detect novel attacks that do not match the known signatures already stored in the database. Anomaly detection is a kind of intrusion detection in which the activities of a system are monitored and these activities are classified as normal or anomalous based on their expected behavior. Tree-based classifiers have been successfully used to separate the abnormal behavior from the normal one. Tree pruning is a machine learning technique used to minimize the size of a decision tree (DT) in order to reduce the complexity of the classifier and improve its predictive accuracy. In this paper, we attempt to prune a DT using particle swarm optimization (PSO) algorithm and apply it to the network intrusion detection problem. The proposed technique is a hybrid approach in which PSO is used for node pruning and the pruned DT is used for classification of the network intrusions. Both single and multi-objective PSO algorithms are used in the proposed approach. The experiments are carried out on the well-known KDD99Cup dataset. This dataset has been widely used as a benchmark dataset for network intrusion detection problems. The results of the proposed technique are compared to the other state-of-the-art classifiers and it is observed that the proposed technique performs better than the other classifiers in terms of intrusion detection rate, false positive rate, accuracy, and precision.

Keywords

Intrusion detection Decision tree Tree pruning Particle swarm optimization 

Notes

Acknowledgements

The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University for its funding of this research through the Research Group Project no. RGP-214.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Software EngineeringFoundation UniversityIslamabadPakistan
  2. 2.King Saud UniversityRiyadhSaudi Arabia
  3. 3.National University of Computer and Emerging SciencesIslamabadPakistan

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