CFP last date
20 March 2024
Call for Paper
April Edition
IJCA solicits high quality original research papers for the upcoming April edition of the journal. The last date of research paper submission is 20 March 2024

Submit your paper
Know more
Reseach Article

Study and Comparison of Feature Selection Approaches for Intrusion Detection

Published on September 2016 by Rajinder Kaur, Monika Sachdeva, Gulshan Kumar
International Conference on Advances in Emerging Technology
Foundation of Computer Science USA
ICAET2016 - Number 2
September 2016
Authors: Rajinder Kaur, Monika Sachdeva, Gulshan Kumar
19ce3442-1060-49ba-b8e3-625aa5ddb63c

Rajinder Kaur, Monika Sachdeva, Gulshan Kumar . Study and Comparison of Feature Selection Approaches for Intrusion Detection. International Conference on Advances in Emerging Technology. ICAET2016, 2 (September 2016), 1-7.

@article{
author = { Rajinder Kaur, Monika Sachdeva, Gulshan Kumar },
title = { Study and Comparison of Feature Selection Approaches for Intrusion Detection },
journal = { International Conference on Advances in Emerging Technology },
issue_date = { September 2016 },
volume = { ICAET2016 },
number = { 2 },
month = { September },
year = { 2016 },
issn = 0975-8887,
pages = { 1-7 },
numpages = 7,
url = { /proceedings/icaet2016/number2/25882-t031/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advances in Emerging Technology
%A Rajinder Kaur
%A Monika Sachdeva
%A Gulshan Kumar
%T Study and Comparison of Feature Selection Approaches for Intrusion Detection
%J International Conference on Advances in Emerging Technology
%@ 0975-8887
%V ICAET2016
%N 2
%P 1-7
%D 2016
%I International Journal of Computer Applications
Abstract

At Present, it is very essential to establish a high level network security to make sure the more trusted and secure communication between various organizations. Network Security provides a platform to secure information channels from the huge amount of network attacks. Intrusion Detection System (IDS) is an estimable tool for the defense mechanism in computer networks. IDS focus on detecting of harmful network traffic that would exploit vulnerability in network system. Feature selection performs a necessary role in intrusion detection process. The dataset extracted in IDS contain a large number of features, in which some of irrelevant, redundant and noisy. These unnecessary features degrades the performance of the IDS. In order to discard irrelevant, redundant & noisy features in the experiment, have need to analyzed different feature selection approaches with various search methods. The pre-processed NSL-KDD dataset is used in experiments for evaluation purpose at WEKA 3. 6. 9 environment tool. By using Bayes Net and Naive Bayes Classifier classify the selected feature dataset. The comparison of all empirical results are done by using different performance metrics. The ultimate goal of work is to increase the overall accuracy of the detection process with minimal number of selected feature dataset and reduced training time.

References
  1. D. Denning, An intrusion-detection model, Software Engineering, IEEE Transactions on SE-13 (2) (1987) 222-232.
  2. G. Stoneburner. , Underlying technical models for information technology security. , NIST Special Publication 800-33 , National Institute of Standards and Technology.
  3. S. Mukherjee, N. Sharma, Intrusion detection using naive bayes classifier with feature reduction, 2nd International Conference on Computer, Communication, Control and Information Technology( C3IT-2012) Procedia Technology 4 (2012) 119- 128
  4. R. Singh, H. Kumar, R. Singla, Analysis of feature selection techniques for network traffic dataset, in: Machine Intelligence and Research Advancement (ICMIRA), 2013 International Conference on, 2013, pp. 42-46.
  5. K. Kumar, G. Kumar, Y. Kumar, Feature selection approach for intrusion detection system, International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE) 2 (5).
  6. T. Garg, Y. Kumar, Combinational feature selection approach for network intrusion detection system, in: Parallel, Distributed and Grid Computing (PDGC), 2014 International Conference 2014, pp. 82-87.
  7. R. Kaur, G. Kumar, K. Kumar, A comparative study of feature selection techniques for intrusion detection, in:
  8. Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference 5 pp. 2120-2124.
  9. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I. H. Witten, The WEKA data mining software: An update, SIGKDD Explorations 11 (1) (2009) 10-18.
  10. KDD, "Kddcup99 intrusion dataset. " [Online]. Available:http://kdd. ics. uci. edu/databases/kddcup99/
Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection System (ids) Feature Selection (fs) Approaches Pre-processing Dataset Bayes Net Naive Bayes Nsl-kdd