A Comparative Study on the Feature Selection Techniques for Intrusion Detection System

Authors

  • D. Selvamani Research Scholar, Department of Computer Science, Mother Teresa Women‟s University, Tamil Nadu, India
  • V. Selvi Assistant Professor, Department of Computer Science, Mother Teresa Women‟s University, Tamil Nadu, India

DOI:

https://doi.org/10.51983/ajcst-2019.8.1.2120

Keywords:

Intrusion Detection, Feature Selection, Information Gain, Gain Ratio, Symmetrical Uncertainty, Chi-Square, Classification, ANN, Naïve Bayes and Support Vector Machine

Abstract

The Intrusion Detection System (IDS) can be used broadly for securing the network. Intrusion detection systems (IDS) are typically positioned laterally through former protecting safety automation, like access control and verification, as a subsequent line of resistance that guards data classifications. Feature selection is employed to diminish the number of features in various applications where data has more than hundreds of attributes. Essential or relevant attribute recognition has converted a vital job to utilize data mining algorithms efficiently in today world situations. This article describes the comparative study on the Information Gain, Gain Ratio, Symmetrical Uncertainty, Chi-Square analysis feature selection techniques with different Classification methods like Artificial Neural Network, Naïve Bayes and Support Vector Machine. In this article, different performance metrics has utilized to choose the appropriate Feature Selection method for better data classification in IDS.

References

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Dataset Source: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.

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Published

15-02-2019

How to Cite

Selvamani, D., & Selvi, V. (2019). A Comparative Study on the Feature Selection Techniques for Intrusion Detection System. Asian Journal of Computer Science and Technology, 8(1), 42–47. https://doi.org/10.51983/ajcst-2019.8.1.2120