CFP last date
20 May 2024
Reseach Article

An Examination of Network Intrusion Detection System Tools and Algorithms: A Review

by Jyoti Harbola, Kunwar Singh Vaisla, Aditya Harbola
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 6
Year of Publication: 2014
Authors: Jyoti Harbola, Kunwar Singh Vaisla, Aditya Harbola
10.5120/16600-6413

Jyoti Harbola, Kunwar Singh Vaisla, Aditya Harbola . An Examination of Network Intrusion Detection System Tools and Algorithms: A Review. International Journal of Computer Applications. 95, 6 ( June 2014), 32-35. DOI=10.5120/16600-6413

@article{ 10.5120/16600-6413,
author = { Jyoti Harbola, Kunwar Singh Vaisla, Aditya Harbola },
title = { An Examination of Network Intrusion Detection System Tools and Algorithms: A Review },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 6 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 32-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume95/number6/16600-6413/ },
doi = { 10.5120/16600-6413 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:18:44.686528+05:30
%A Jyoti Harbola
%A Kunwar Singh Vaisla
%A Aditya Harbola
%T An Examination of Network Intrusion Detection System Tools and Algorithms: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 6
%P 32-35
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays secured information communication has becoming at risk. Millions of users using the Internet at any instant of time and taking full use of the application's, services. DDoS flooding attacks are complex attempts to block the legitimate users. The Attacker normally gains access to a large number of computers by breaching their security loopholes and then they launch their attack to the target machine by these compromised machines. Intrusion Detection Systems have gained quick growth in command, scope and complexity. All IDS share an analogous primary structure: agents. Modern boost in malevolent network activity have hurried the need for IDS with global scope. A single IDS power can be grown by connecting an attack relationship engine with a database of events collected by distributed agents. This will help to provide global and single view of existing and rising attacks and will allow fast warning and ease development of countermeasures. A large number of distributed IDS with global and wide scope have been active for several years; three of these are discussed and compared with each other in this paper.

References
  1. Aneetha, S. , Indhu, T. S. & Bose, S. (2012). Hybrid Network Intrusion Detection System Using Expert Rule Based Approach. Paper presented at the CCSEIT '12 Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology Pages 47-51,ACM New York, NY, USA ©2012
  2. Casella, E. L. Lehmann and G. (1998). Theory of Point Estimation Springer Texts in Statistics Vol. 2nded. (pp. 590 p). doi:10. 1007/b98854
  3. M. Sadeghi, F. Khosravi, K. Atefi, M. Barati. (2012). Security Analysis of Routing Protocols in Wireless Sensor Networks International Journal of Computer Science Issues, 9, 465-472
  4. Carter, Earl. (2001). Cisco Secure Intrusion Detection System (Vol. 1). 800 East 96th Street, Indianapolis, Indiana 46240: Pearson Education, Cisco Press.
  5. Yuebin Bail, Hidetsune Kobayashil ( March 27-29, 2003). Detection Systems: Technology and Development. Paper presented at the 17th International Conference on Advanced Information Networking and Applications (AINA'03), Xi'an, China.
  6. Sharmila Devi, Ritu Nagpal. (2012). Intrusion Detection System Using Genetic Algorithm-A Review. International Journal of Computing & Business Research.
  7. Whitley, Darrell. (1992). Foundations of Genetic Algorithms and Classifier. Morgan Kaufmann Publishers Inc. , 297-318.
  8. Snort(software); http://en. wikipedia. org/ wiki/Snort_% 28 software%29
  9. InfoWorld, The greatest open source software of all time, 2009; http://www. infoworld. com/d/open-source/greatest-open-source-software-all-time-776?source=fssr
  10. SecTools. Org: Top 125 Network Security Tools; http:// sectools . org/tag/ids/
  11. Sectools. Org: 2006 Results; http://sectools. org/tools 20 06. html
  12. Houque,Mukit,Bikas"An mplementation of Intrusion Detection System Using Genetic Algorithm" IJNSA,Vol. 4, No. 2,March 2012
  13. http://en. wikipedia. org/wiki/Support_vector_machine
  14. Guggenberger, Andre. (2008). Another Introduction to Support Vector Machines. Retrieved from http://mindthegap. googlecode. com/files/ AnotherIntroductionSVM. pdf
  15. P. Berkhin. A Survey of Clustering Data Mining Techniques. Grouping Multidimensional Data, p. 25–71, 2002
  16. A. Abraham and R. Jain. Soft Computing Models for Network Intrusion Detection Systems. Classification and Clustering for Knowledge Discovery Studies in Computational Intelligence, p. 191–207, 2005
  17. S. Abe. Support Vector Machines for pattern classification. London, Springer, 2005
  18. N. Cristiani and J. Shawe-Taylor. An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge, Cambridge University Press, 2000.
  19. D. H. Fisher. Knowledge Acquisition Via Incremental Conceptual Clustering. Kluwer Academic Publisher, 1987.
  20. Anithakumari, S. ; Chithraprasad, D. , "An Efficient Pattern Matching Algorithm for Intrusion Detection Systems," Advance Computing Conference, 2009. IACC 2009. IEEE International , vol. , no. , pp. 223,227, 6-7 March 2009
  21. Bhavani sunke, Research and Analysis of Network Intrusion Detection systems, Internet, 1-88, 2008.
  22. B. Raju1 and B. SrinivasNetwork Intrusion Detection System Using KMP Pattern Matching Algorithm, IJCST,33-36, January 2012.
  23. Aditya Harbola et. al. "Green computing research challenges: A review", IJARCSSE, Volume 3, Issue 10, October 2013
Index Terms

Computer Science
Information Sciences

Keywords

DDOS Network attacks IDS IDS algorithms IDS tools