A Novel Intrusion Detection System Using Artificial Neural Networks and Feature Subset Selection

Authors

  • L. Haripriya
  • M. A. Jabbar
  • B. Seetharamulu

DOI:

https://doi.org/10.14419/ijet.v7i4.6.20458

Published:

2018-09-25

Keywords:

ANN, Back propagation, IDS, Machine Learning, KYOTO

Abstract

The growth of internet and network technologies has been increasing day by day.With the increase of these technologies, attacks and intrusions are also increasing. The prevention of these attacks has become an task. Intrusion Detection System (IDS) provides prevention against these attacks. Data Mining and Machine Learning techniques are used for IDS to reduce error rate and to improve accuracy and detection rate. In this paper, we proposed a novel Artificial Neural Network (ANN) classifier using Back propagation algorithm to model IDS. ANN is widely used supervised classifier for IDS. The performance of our model is evaluated by conducting experiments on KYOTO data set which is refined version of KDD99 data set. Empirical results show that proposed model is efficient with high detection rate and accuracy.

 

 

References

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How to Cite

Haripriya, L., A. Jabbar, M., & Seetharamulu, B. (2018). A Novel Intrusion Detection System Using Artificial Neural Networks and Feature Subset Selection. International Journal of Engineering & Technology, 7(4.6), 181–184. https://doi.org/10.14419/ijet.v7i4.6.20458
Received 2018-09-29
Accepted 2018-09-29
Published 2018-09-25