Open AccessA Novel Intrusion Detection System Using Artificial Neural Networks and Feature Subset Selection
Author(s) -
L. Haripriya,
M. A. Jabbar,
B. Seetharamulu
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.6.20458
Subject(s) - computer science , intrusion detection system , feature selection , artificial neural network , artificial intelligence , machine learning , data mining , classifier (uml) , word error rate , the internet , pattern recognition (psychology) , world wide web
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.