Intrusion detection using artificial neural networks with best set of features
Joint Authors
Jayakumar, Kaliappan
Revathi, Thiagarajan
Karpagam, Sundararajan
Source
The International Arab Journal of Information Technology
Issue
Vol. 12, Issue 6A(s) (31 Dec. 2015), pp.728-734, 7 p.
Publisher
Publication Date
2015-12-31
Country of Publication
Jordan
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
An Intrusion Detection System (IDS) monitors the behavior of a given environment and identifies the activities are malicious (intrusive) or legitimate (normal) based on features obtained from the network traffic data.
In the proposed method, instead of considering all features for intrusion detection and wasting up the time in analyzing it, only the relevant feature for the particular attack is selected and intrusion detection is done with help of supervised learning Neural Network (NN).
The feature selection is done with the help of information gain algorithm and genetic algorithm.
The Multi Layer Perceptron (MLP) supervised NN is used to train the relevant features alone in our proposed system.
This system improves the Detection Rate (DTR) for all types of attacks when compared to Intrusion detection system which uses all features and selected features using genetic algorithm with MLP NN as the classifier.
Our proposed system results, in detecting intrusions with higher accuracy, especially for Remote to Local (R2L), User to Root (U2R) and Denial of Service (DoS) attacks.
American Psychological Association (APA)
Jayakumar, Kaliappan& Revathi, Thiagarajan& Karpagam, Sundararajan. 2015. Intrusion detection using artificial neural networks with best set of features. The International Arab Journal of Information Technology،Vol. 12, no. 6A(s), pp.728-734.
https://search.emarefa.net/detail/BIM-655012
Modern Language Association (MLA)
Jayakumar, Kaliappan…[et al.]. Intrusion detection using artificial neural networks with best set of features. The International Arab Journal of Information Technology Vol. 12, no. 6A (Dec. 2015), pp.728-734.
https://search.emarefa.net/detail/BIM-655012
American Medical Association (AMA)
Jayakumar, Kaliappan& Revathi, Thiagarajan& Karpagam, Sundararajan. Intrusion detection using artificial neural networks with best set of features. The International Arab Journal of Information Technology. 2015. Vol. 12, no. 6A(s), pp.728-734.
https://search.emarefa.net/detail/BIM-655012
Data Type
Journal Articles
Language
English
Notes
Includes appendix : p. 733-734
Record ID
BIM-655012