Nonlinear Feature Transformation and Genetic Feature Selection: Improving System Security and Decreasing Computational Cost

  • Taghanaki, Saeid Asgari (Department of Computer and Electrical Engineering, Majlesi Branch, Islamic Azad University) ;
  • Ansari, Mohammad Reza (Department of Computer and Electrical Engineering, Semirom Branch, Islamic Azad University) ;
  • Dehkordi, Behzad Zamani (Department of Computer and Electrical Engineering, Shahrekord Branch, Islamic Azad University) ;
  • Mousavi, Sayed Ali (Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University)
  • Received : 2012.04.10
  • Accepted : 2012.10.01
  • Published : 2012.12.31


Intrusion detection systems (IDSs) have an important effect on system defense and security. Recently, most IDS methods have used transformed features, selected features, or original features. Both feature transformation and feature selection have their advantages. Neighborhood component analysis feature transformation and genetic feature selection (NCAGAFS) is proposed in this research. NCAGAFS is based on soft computing and data mining and uses the advantages of both transformation and selection. This method transforms features via neighborhood component analysis and chooses the best features with a classifier based on a genetic feature selection method. This novel approach is verified using the KDD Cup99 dataset, demonstrating higher performances than other well-known methods under various classifiers have demonstrated.


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