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Detection and Classification of Intrusions using Fusion Probability of HMM

by Hemlata Sukhwani, Shwaita Kodesia, Sanjay Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 12
Year of Publication: 2014
Authors: Hemlata Sukhwani, Shwaita Kodesia, Sanjay Sharma
10.5120/18127-9213

Hemlata Sukhwani, Shwaita Kodesia, Sanjay Sharma . Detection and Classification of Intrusions using Fusion Probability of HMM. International Journal of Computer Applications. 103, 12 ( October 2014), 26-30. DOI=10.5120/18127-9213

@article{ 10.5120/18127-9213,
author = { Hemlata Sukhwani, Shwaita Kodesia, Sanjay Sharma },
title = { Detection and Classification of Intrusions using Fusion Probability of HMM },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 12 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 26-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number12/18127-9213/ },
doi = { 10.5120/18127-9213 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:34:23.163771+05:30
%A Hemlata Sukhwani
%A Shwaita Kodesia
%A Sanjay Sharma
%T Detection and Classification of Intrusions using Fusion Probability of HMM
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 12
%P 26-30
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Intrusion detection system is a technique of identifying unwanted packets that creates harm in the network; hence various IDS are implemented for the security of network traffic flow. Here in this paper an efficient technique of identifying intrusions is implemented using hidden markov model and then classification of these intrusions is done. The methodology sis applied on KDDCup 99 dataset where the dataset is first clustered using K-means algorithms and then a number of attributes is selected which are used for the detection of intrusion is passed to the HMM, after calculating probability from each of the states, these probabilities are fused to get the resultant final probability and also overall probability is calculated from dataset on the basis of which intrusions are classified as low, medium or high.

References
  1. S. Forrest, P. D'haeseleer, and P. Helam, "An immunological approach to change detection: Algorithms, analysis and implications". In Proceedings of the IEEE Symposium on Security and Privacy, IEEE Computer Society, vol. 110, 1996
  2. C. Warrender, S. Forrest, and B. Pearlmutter, "Detecting intrusions using system calls: Alternate data models," In Proceedings of the IEEE ISRSP. IEEE Computer Society, 1999, pp. 133 – 145.
  3. S. Forrest, S. A. Hofmeyr, A. Somayaji. and T. A. Longstaff, "A sense of self for unix processes," In Proceedings of the IEEE ISRSP, 1996, pp120 – 128.
  4. Warusia Yassin, Nur Izura Udzir1, Zaiton Muda, and Md. Nasir Sulaiman "Anomaly based intrusion detection through K-means clustering and naives bayes classification". Proceedings of the 4th International Conference on Computing and Informatics, ICOCI 2013 28-30 August, 2013 Sarawak, Malaysia.
  5. A. K. Ghosh, and A. Schwartzbard, "A study in using neural networks for anomaly and misuse detection," In Proceedings of the 8th USENIX Security Symposium, 1999.
  6. Shui Yu, Wanlei Zhou, Robin Doss, Weijia Jia,"Traceback of DDoS Attacks Using Entropy Variations" IEEE/ACM Tran. ON Parallel and Distributed Systems" vol. 22, no. 3, March 2011.
  7. Ghorbani Ali A. , Lu Wei, Tavallaee Mahbod, Ghorbani Ali A. , Lu Wei, and Tavallaee Mahbod, 2010. Intrusion response. Jajodia Sushil, editor, Network Intrusion Detection and Prevention, volume 47 of Advances in Information Security, pages 185–198. Springer US ISBN 978-0-387-88771-5.
  8. Rash Michael, Orebaugh Angela D. , Clark Graham, Pinkard Becky, and Babbin Jake, 2005. Intrusion Prevention and Active Response: Deployment Network and Host IPS. Syngress.
  9. Scarfone Karen and Mell Peter, February 2007. Guide to intrusion detection and prevention systems (IDPS). Recommendations of the National Institute of Standards and Technology sp800-94, NIST, Technology Administration, Department of Commerce, USA, 2007.
  10. Stakhanova Natalia, Basu Samik, and Wong Johnny, 2007. A taxonomy of intrusion response systems. International Journal of Information and Computer Security, 1(1/2):169–184.
  11. Tucker C. J. , Furnell S. M. , Ghita B. V. , and Brooke P. J. , 2007. A new taxonomy for comparing intrusion detection systems, Internet Research, 17:88–98.
  12. Lazarevic Aleksandar, Kumar Vipin, and Srivastava Jaideep, 2005. Intrusion detection: A survey. Kumar Vipin, Srivastava Jaideep, and Lazarevic Aleksandar, editors, Managing Cyber Threats, volume 5 of Massive Computing, pages 19–78. Springer US ISBN 978-0-387-24230-9
  13. Estevez-Tapiador Juan M. , Garcia-Teodoro Pedro, and Diaz-Verdejo Jesus E. , 2004. Anomaly detection methods in wired networks: A survey and taxonomy. Computer Communications, 27(16):1569–1584. ISSN 0140-3664.
  14. Mohammad Khubeb Siddiqui and Shams Naahid "Analysis of KDD CUP 99 Dataset using Clustering based Data Mining",International Journal of Database Theory and Application.
  15. Megha Bandgar, Komal dhurve, Sneha Jadhav,Vick Kayastha, Prof. T. J Parvat, "Intrusion Detection System using Hidden Markov Model (HMM)", IOSR Journal of Computer Eng. (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278- 8727Volume 10, Issue 3 (Mar. - Apr. 2013), PP 66-70 www. iosrjournals. org.
  16. Huang et. al. Spoken Language Processing. Prentice Hall PTR
Index Terms

Computer Science
Information Sciences

Keywords

IDS Anomaly HMM Behavioral Distance

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