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Reseach Article

A Survey: Intelligent Intrusion Detection System in Computer Security

by Parveen Sadotra, Chandrakant Sharma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 151 - Number 3
Year of Publication: 2016
Authors: Parveen Sadotra, Chandrakant Sharma
10.5120/ijca2016911699

Parveen Sadotra, Chandrakant Sharma . A Survey: Intelligent Intrusion Detection System in Computer Security. International Journal of Computer Applications. 151, 3 ( Oct 2016), 18-22. DOI=10.5120/ijca2016911699

@article{ 10.5120/ijca2016911699,
author = { Parveen Sadotra, Chandrakant Sharma },
title = { A Survey: Intelligent Intrusion Detection System in Computer Security },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2016 },
volume = { 151 },
number = { 3 },
month = { Oct },
year = { 2016 },
issn = { 0975-8887 },
pages = { 18-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume151/number3/26213-2016911699/ },
doi = { 10.5120/ijca2016911699 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:56:06.927279+05:30
%A Parveen Sadotra
%A Chandrakant Sharma
%T A Survey: Intelligent Intrusion Detection System in Computer Security
%J International Journal of Computer Applications
%@ 0975-8887
%V 151
%N 3
%P 18-22
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now a day’s fast broadcast of computer networks has changed the perspective of network security. An easy availability of circumstances cause computer network as vulnerable beside numerous threats from hackers. Threats to networks are various and possibly devastating. Up to the instant, researchers have established Intrusion Detection Systems (IDS) proficient of identifying attacks in numerous presented environments. A boundlessness of approaches for misuse detection as well as anomaly detection has been functional. Numerous of the tools projected are balancing to each other, since for different kind of surroundings some methods achieve better than others. This paper presents an evaluation of intrusion detection systems that is then used to study and classify them. The taxonomy involves of the detection principle, and another of positive working features of the intrusion detection system.

References
  1. R. Base, P. Mell, “Special publication on intrusion detection systems”, NIST Infidel, Inc., National Institute of Standards and Technology, Scotts Valley, CA, 2001.
  2. J. Anderson, “An introduction to neural networks”, Cambridge: MIT Press, 1995.
  3. P.G. Teodoro, J.D. Verdejo, G.M. Fernandez, E. Vazquez, “Anomaly-based network intrusion detection: techniques, systems and challenges”, Computers Security, 2009.
  4. J. Viinikka, H. Debar, L. Mé, A. Lehikoinen, M. Tarvainen, “Processing intrusion detection alert aggregates with time series modeling”, Information Fusion Journal, vol. 10(4), 2009.
  5. R. Vaarandi, “Real-time classification of IDS alerts with data mining techniques”, in Proc. of MILCOM Conference, 2009.
  6. K. Julisch, “Clustering intrusion detection alarms to support root cause analysis”, ACM Trans. Inf. Syst. Secur. 6, 2003
  7. G.C. Tjhai, S.M. Furnell, M. Papadaki, N.L. Clarke, “A preliminary two-stage alarm correlation and filtering system using SOM neural network and K-means algorithm”, Computers & Security 29, 2010.
  8. Yogita B. Bhavasar, Kalyani C. Waghmare “Intrusion Detection System Using Data Mining Technique: Support Vector Machine” 2013 International Journal of Emerging Technology and Advance Engineering volume 3, Issue 3, March 2013.
  9. WenkeLee ,Salvatore J. Stolfo “Adaptive Intrusion Detection: a Data Mining Approach” 2000
  10. HuyAnh Nguyen, Deokjai Choi “Application of Data Mining to Network Intrusion Detection: Classifier Selection Model”
  11. S.A.Joshi, VarshaS.Pimprale, “Network Intrusion Detection System (NIDS) based on Data Mining”, International Journal of Engineering Science and Innovative Technology, Vol. 2, No. 1, January 2013, ISSN. 2319 5967.
  12. Sushil Kumar Chaturvedi, Prof. VineetRichariya. Prof. NirupamaTiwari, “Anomaly Detection in Network using Data mining Techniques”, International Journal of Emerging Technology and Advanced Engineering, Vol. 2, No. 5, May 2012, ISSN. 2250-2459.
  13. OmprakashChandrakar, Rekha Singh, Dr. LalBihariBarik, “Application of Genetic Algorithm in IntrusionDetection System”, International Institute for Science, Technology and Education, Vol. 4, No. 1, 2014, ISSN. 2224-5774.
  14. A.R. Jakhale, G.A. Patil, “Anomaly Detection System by Mining Frequent Pattern using Data Mining Algorithm from Network Flow”, International Journal of Engineering Research and Technology, Vol. 3, No.1, January 2014, ISSN. 2278-0181.
  15. R. Venkatesan, Dr. R. Ganesan, Dr. A. Arul Lawrence Selvakumar., “A Survey on Intrusion Detection using Data Mining Techniques”, International Journal of Computers andDistributed Systems, Vol. 2, No. 1, December 2012, ISSN. 2278-5183.
  16. Abhilasha A Sayar, Sunil. N. Pawar, Vrushali Mane., “A Review of Intrusion Detection System in Computer Network”, International Journal of Computer Science and Mobile Computing, Vol. 3, No. 2, February 2014, pp. 700 - 703.
Index Terms

Computer Science
Information Sciences

Keywords

IDS security Network WSN SVM