Call for Paper - December 2021 Edition
IJCA solicits original research papers for the December 2021 Edition. Last date of manuscript submission is November 20, 2021. Read More

An Intrusion Detection System using KNN-ACO Algorithm

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Satyendra Vishwakarma, Vivek Sharma, Ankita Tiwari
10.5120/ijca2017914079

Satyendra Vishwakarma, Vivek Sharma and Ankita Tiwari. An Intrusion Detection System using KNN-ACO Algorithm. International Journal of Computer Applications 171(10):18-23, August 2017. BibTeX

@article{10.5120/ijca2017914079,
	author = {Satyendra Vishwakarma and Vivek Sharma and Ankita Tiwari},
	title = {An Intrusion Detection System using KNN-ACO Algorithm},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2017},
	volume = {171},
	number = {10},
	month = {Aug},
	year = {2017},
	issn = {0975-8887},
	pages = {18-23},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume171/number10/28291-2017914079},
	doi = {10.5120/ijca2017914079},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

With the remarkable enlargement of the usage of computers through the network and expansion in application running on several platform captures the consideration toward network security. This hypothesis exploits security susceptibilities on the entire computer systems that are technically challenging and expensive to resolve. Therefore, intrusion is employs as a key to conciliate reliability, availability and privacy/confidentiality of a computer resource. An Intrusion Detection System (IDS) participates a noteworthy responsibility in detecting anomalies and attacks over’s network. In this research work, data mining conception is integrated with IDS to sort assured the relevant, concealed information of interest for the user efficiently and with fewer implementation times. Four concerns likely Classification of Data, Lack of Labeled Data, Extreme Level of Human Interaction and Effectiveness of D-DOS are being resolved by using the projected algorithms like EDADT algorithm, Semi-Supervised Approach, Hybrid IDS model and transforming HOPERAA Algorithm respectively. In this paper, proposes a SVM and KNN-ACO method for the intrusion detection and the analysis of this is perform using KDD1999 Cup dataset. This proposed algorithm shows improved precision and concentrated false alarm rate when matched with existing algorithms.

References

  1. Axelsson, S., “Intrusion Detection Systems: A Taxonomy and Survey,” Technical Report No 99-15, Dept. of Computer Engineering, Chalmers University of Technology, Sweden, March 2000.
  2. Lunt, T. F., “Detecting Intruders in Computer Systems,” in proceeding of 1993 Conference on Auditing and Computer Technology, 1993.
  3. Sundaram, A. “An Introduction to Intrusion Detection,” The ACM Student Magazine, Vol.2, No.4, April 1996. http://www.acm.org/crossroads/xrds2-4/xrds2-4.html.
  4. Porras, P. A., “STAT: A State Transition Analysis Tool for Intrusion Detection,” MSc Thesis, Department of Computer Science, University of California Santa Babara, 1992
  5. Dorothy E. Denning, “An Intrusion Detection Model,” In IEEE Transactions on Software Engineering, Vol.SE 13, Number 2, page 222-232, February 1987.
  6. Divyatmika, Manasa Sreekesh, “A Two-tier Network based Intrusion Detection System Architecture using Machine Learning Approach”, International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) – 2016 in proceeding of IEEExplore.
  7. Wenke Lee and Salvatore J. Stolfo “Data mining approaches for intrusion detection”, In Proceedings of the7th USENIX Security Symposium - Volume 7, SSYM’98, pages 6–6, Berkeley, CA, USA, 1998.
  8. Rajesh Wankhede, Vikrant Chole, “Intrusion Detection System using Classification Technique”, International Journal of Computer Applications (0975 – 8887) Volume 139 – No.11, April 2016.
  9. Mrutyunjaya Panda and Manas Ranjan Patra, “Comparative Study Of Data Mining Algorithms For Network Intrusion Detection” First International Conference on Emerging Trends in Engineering and Technology, pp 504-507, IEEE, 2008.
  10. G.V. Nadiammai, M. Hemalatha, “Effective approach toward Intrusion Detection System using data mining techniques”, Egyptian Informatics Journal 2015, in proceeding Elsevier Pp 37–50.
  11. Mouhammd Alkasassbeh, Ahmad B.A Hassanat, Ghazi Al-Naymat, Mohammad Almseidin, “ Detecting Distributed Denial of Service Attacks Using Data Mining Techniques”, International Journal of Advanced Computer Science and Applications, Vol. 7, No. 1, 2016.
  12. M. R. Norouzian and S. Merati, “Classifying attacks in a network intrusion detection system based on artificial neural networks,” in Advanced Communication Technology (ICACT), 2011 13th International Conference on, pp. 868–873, IEEE, 2011.
  13. JianfengPu, Lizhi Xiao, Yanzhi Li and Xingwen Dong “A Detection Method of Network Intrusion Based onSVM and Ant Colony Algorithm”, National Conference on Information Technology and Computer Science (CITCS 2012) Published by Atlantis Press.
  14. Ayman I. Madbouly, Amr M. Gody, Tamer M. Barakat, “Relevant Feature Selection Model Using DataMining for Intrusion Detection System”, International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 10 - Mar 2014.
  15. S. Selvakani Kandeeban, Dr. R.S. Rajesh : “a genetic algorithm based elucidation for improving intrusion detection through condensed feature set by KDD99 dataset”, information and knowledge management ISSN 2224-5758, ISSN 2224-896X Vol. 1, No.1, 2011, www.iiste.org.
  16. Mouaad KEZIH, Mahmoud TAIBI “evaluation effectiveness of intrusion detection system with reduced dimension using data mining classification tools”, 2nd International Conference on Systems and Computer Science (ICSCS) Villeneuve d'Ascq, France, August 26-27, 2013; 978-1-4799-2022.
  17. VivekNandanTiwari, Prof. SatyendraRathore, Prof. KailashPatidar “Enhanced Method for Intrusion Detection over KDD Cup 99 Dataset”, International Journal of Current Trends in Engineering & Technology, Volume: 02, Issue: 02 (MAR-APR, 2016), ISSN: 2395-3152.
  18. Mrs. D. Shona, M. Senthilkumar “An Ensemble Data Preprocessing Approach for Intrusion Detection System Using variant Firefly and Bk-NN Techniques”, International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 6 (2016) pp 4161-4166.

Keywords

Precision, Data Mining, Intruders, MATLAB, KDDCUP’99 Dataset