CFP last date
22 April 2024
Reseach Article

A Comparative Analysis of Different Classification Techniques for Intrusion Detection System

by Neha Maharaj, Pooja Khanna
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 17
Year of Publication: 2014
Authors: Neha Maharaj, Pooja Khanna
10.5120/16687-6806

Neha Maharaj, Pooja Khanna . A Comparative Analysis of Different Classification Techniques for Intrusion Detection System. International Journal of Computer Applications. 95, 17 ( June 2014), 22-26. DOI=10.5120/16687-6806

@article{ 10.5120/16687-6806,
author = { Neha Maharaj, Pooja Khanna },
title = { A Comparative Analysis of Different Classification Techniques for Intrusion Detection System },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 17 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 22-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume95/number17/16687-6806/ },
doi = { 10.5120/16687-6806 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:19:42.027492+05:30
%A Neha Maharaj
%A Pooja Khanna
%T A Comparative Analysis of Different Classification Techniques for Intrusion Detection System
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 17
%P 22-26
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Intrusion Detection Systems are the network security mechanism that monitors network and system activities for malicious actions. It becomes indispensable tool to keep information system safe and reliable. The primary goal of intrusion detection is to model usual application behaviour, so that we can recognize attacks by their peculiar effects without raising too many false alarms. In this work data mining techniques are used for intrusion detection to identify normal and malicious actions on the system. The whole work considered Intrusion detection as a data analysis process. The Weka tool is used for analysis on KDD Cup [1] dataset. Algorithm REPTree & VFI(Voting Feature Interval) are chosen in this work with full training set and percentage split in which dataset can be divided into two ratio, and then one part is used as training set and the other part is applied as test set. The ROC curve is implemented for the comparison of classification algorithms.

References
  1. http://kdd. ics. uci. edu/databases/kddcup99/kddcup99. html
  2. Lei Li, De-Zhang Yang, Fang-Cheng Shen, A Novel Rule-based Intrusion Detection System Using Data Mining, 978-1-4244-5540-9/10 ©2010 IEEE
  3. Desheng Fu, Shu Zhou, Ping Guo, The Design and Implementation of a Distributed Network Intrusion Detection System Based on Data Mining, World Congress on Software Engineering.
  4. Chai Wenguang, Tan Chunhui, Duan Yuting, Research of Intelligent Intrusion Detection System Based On Web Data Mining Technology, 2011.
  5. G. V. Nadiammai, M. Hemalatha, "Effective approach toward Intrusion Detection System using data mining techniques", Cairo University Egyptian Informatics Journal.
  6. M. Moorthy and Dr. S. Sathiyabama. Study of Intrusion Detection using Data Mining. IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM), ISBN: 978-81-909042-2-3. 2012
  7. Mrutyunjaya P. , Manas R. Patra, " A Comparative Study of Data Mining Algorithms for Network Intrusion Detection ", Berhampur University, 2007
  8. R. A. Maxion and R. R. Roberts, Proper Use of ROC Curves in Intrusion/Anomaly Detection, School of Computing Science, University of New castle upon Tyne.
  9. Jun Zheng and Ming-zeng hu, Intrusion Detection of Dos/DDos and Probing Attacks for Web Services, Advances in Web-Age Information Management Lecture Notes in Computer Science Volume 3739, 2005.
  10. Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu, and Ali A. Ghorbani, A Detailed Analysis of the KDD CUP 99 Data Set, 978-1- 4244-3764-1/09©2009 IEEE.
  11. Mohammad Khubeb Siddiqui and Shams Naahid, Analysis of KDD CUP 99 Dataset using Clustering based Data Mining, International Journal of Database Theory and Application Vol. 6, No. 5 (2013).
  12. Prof. N. S. Chandolikar, Prof. (Dr. ) V. D. Nandavadekar, Selection of Relevant Feature for Intrusion Attack Classification by Analyzing KDD Cup 99, MIT International Journal of Computer Science & Information Technology, Vol. 2, No. 2, Aug. 2012, pp. (85-90) ISSN No. 2230-7621 © MIT Publications.
  13. Honghu Liu, Gang Li, Testing Statistical Significance of the Area under a Receiving Operating Characteristics Curve for Repeated Measures Design with Bootstrapping, Journal of Data Science 3 (2005),257-278.
  14. James A. Hanley, McNeil, The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve, RADIOLOY'. Vol. l-1. No. l. Pages 29 April. 1982.
  15. John E. Gaffney Jr. Jacob W. Ulvila, Evaluation of Intrusion Detectors: A Decision Theory Approach, 0-7695-1046-9(C) 2001 IEEE.
  16. Alvaro A. C´ardenas, John S. Baras and Karl Seamon, A Framework for the Evaluation of Intrusion Detection Systems, ACM Transactionson Computational Logic, Vol. ?, No. ?, ? 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Classification technique Receiver operating characteristic (ROC) curves AUC