CFP last date
20 December 2024
Reseach Article

Classification Techniques for Intrusion Detection – An Overview

by P. Amudha, S. Karthik, S. Sivakumari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 76 - Number 16
Year of Publication: 2013
Authors: P. Amudha, S. Karthik, S. Sivakumari
10.5120/13334-0928

P. Amudha, S. Karthik, S. Sivakumari . Classification Techniques for Intrusion Detection – An Overview. International Journal of Computer Applications. 76, 16 ( August 2013), 33-40. DOI=10.5120/13334-0928

@article{ 10.5120/13334-0928,
author = { P. Amudha, S. Karthik, S. Sivakumari },
title = { Classification Techniques for Intrusion Detection – An Overview },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 76 },
number = { 16 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 33-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume76/number16/13334-0928/ },
doi = { 10.5120/13334-0928 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:48:37.013010+05:30
%A P. Amudha
%A S. Karthik
%A S. Sivakumari
%T Classification Techniques for Intrusion Detection – An Overview
%J International Journal of Computer Applications
%@ 0975-8887
%V 76
%N 16
%P 33-40
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Security is becoming a critical part of organizational information systems and security of a computer system or network is compromised when an intrusion takes place. In the field of computer networks security, the detection of threats or attacks is nowadays a critical problem to solve. Intrusion Detection Systems (IDS) have become a standard component in network security infrastructures and is an essential mechanism to protect computer systems from many attacks. In recent years, intrusion detection using data mining have attracted researchers more and more interests. Different researchers propose a different algorithm in different categories. Classifier construction is another research challenge to build an efficient intrusion detection system. KDDCup 1999 intrusion detection dataset plays a key role in fine tuning intrusion detection system and is most widely used by the researchers working in the field of intrusion detection. This paper presents an overview of intrusion detection, KDDCup'99 dataset and detailed analysis of classification techniques used in intrusion detection.

References
  1. Ben Amor, Benferhat, Elouedi, Naive Bayes vs. Decision Trees in Intrusion Detection Systems, Proc. of the 2004 ACM symposium on applied computing, 2004, pp. 420–424.
  2. Bennett K. P and Campbell C. , Support Vector Machines: Hyper plane, SIGKDD Explorations, vol. 2, issue 2, 2000, pp. 1-13.
  3. Bouzida Y, Cuppens F, Neural networks vs. decision trees for intrusion detection, In IEEE / IST Workshop on Monitoring, Attack Detection and Mitigation, 2006.
  4. Breiman L, Random Forests, Machine Learning, vol. 45, no. 1, 2001, pp. 5-23.
  5. Chawla N. V, Bowyer K. W, Hall L. O, Kegelmeyer W. P, Smote: Synthetic minority oversampling technique, Journal of Artificial Intelligence Research, vol. 16, 2002, pp. 321–357.
  6. Chen M. S. , Han J and Yu Philip S. , Data Mining: An Overview from a Database Perspective, IEEE Transactions on Knowledge and Data Engineering, vol. 8,No. 6,1996,pp. 866-883.
  7. Christine Dartigue, Hyun IK Jang, Wenjun Zeng, A New data-mining based approach for network Intrusion detection, Proc. of Seventh Annual Communication Networks and Services Research Conference, 2009, pp. 372-377.
  8. Cortes, Vapnik, Support-vector networks, Machine Learning, vol. 20, 1995, pp. 273–297.
  9. Denning D. E, An intrusion-detection model, IEEE Transactions on Software Engineering, vol. SE-13, no. 2, pp. 222-232.
  10. Dewan Md. Farid, Nouria Harbi, Mohammad Zahidur Rahman , Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection, Proc. of Intl. Journal of Network Security & Its Applications (IJNSA), Volume 2, Number 2, 2010, pp. 12-25.
  11. Dokas, P, Ertoz, L, Lazarevic A, Srivastava J, Tan P. N ,Data mining for network intrusion detection, Proceeding of NGDM, 2002, pp. 21–30.
  12. Domingos P. and Pazzani M. , Beyond Independence: Conditions for the optimality of the simple Bayesian Classifier, In proceedings of the 13th Intnl. Conference on Machine Learning, 1996, pp. 105-110.
  13. Fan, W. , Stolfo, S. , Zhang, J. , & Chan, P. , Adacost: Misclassi?cation cost-sensitive boosting, ICML, 1999.
  14. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, From Data Mining to Knowledge Discovery in Databases, American Association for Artificial Intelligence, 1996, pp. 37-54.
  15. Foster Provost, Tom Fawcett, Robust Classification for Imprecise Environment, 2000, pp. 1-38, Kluwer Academic Publishers.
  16. Frawley, Gregory Piatetsky-Shapiro, Christopher J Matheus, Knowledge Discovery in Databases: an Overview, AI Magazine Vol. 13 No. 3, 1991, pp. 57-70.
  17. Gharibian F, Ghorbani A. A , Comparative Study of Supervised Machine Learning Techniques for Intrusion Detection, Proc. of the Fifth Annual Conference on Communication Networks and Services Research, 2007, pp. 350–358.
  18. Hany M. Harb, Abeer S. Desuky, AdaBoost Ensemble with Genetic Algorithm Post Optimization for Intrusion Detection, Intl. Journal of Computer Science, vol. 8, issue 5, no. 1, 2011, pp. 28-33.
  19. Hu W, Liao Y, Vemuri V. R , Robust Anomaly Detection Using Support Vector Machines, Proc. of Intl. Conference on Machine Learning and Applications, 2003, Morgan Kaufmann.
  20. Huy Anh Nguyen, Deokjai Choi, Application of Data Mining to Network Intrusion Detection: Classifier Selection Model, 2008, pp. 399-408, Springer-Verlag.
  21. Jiong Zhang, Mohammad Zulkernine, Anwar Haque, Random-Forests-Based Network Intrusion Detection Systems, IEEE Transactions on Systems, Man, And Cybernetics—Part C: Applications and Reviews, vol. 38, no. 5, 2008, pp. 649-659.
  22. Juan J Rodriguez, Ludmila I Kuncheva, Carlos J Alonso, Rotation forest: A new classifier ensemble method, IEEE Transactions on Pattern Analysis and Machine Intelligence,2006, pp. 1619-1630
  23. KDD99, KDDCup 1999 data, 1999, http://kdd. ics. uci. edu/ Databases/kddcup99/10 percent. gz.
  24. Khan L, Awad M, Thuraisingham B, A new intrusion detection system using support vector machines and hierarchical clustering, The VLDB Journal, vol. 16, 2007, pp. 507–521.
  25. Langley P, Sage S, Induction of selective Bayesian classifiers, Proc. of the Tenth Conference on Uncertainty in Artificial Intelligence , 1994,pp. 399-406, Seattle, WA: Morgan Kaufmann.
  26. Lee W, Stolfo S. J, Mok K. W. , A Data Mining Framework for Building Intrusion Detection Models, In Proc of IEEE Symposium on Security and Privacy, 1999, pp. 120-132.
  27. Mrudula Gudadhe, Prakash Prasad, Kapil Wankhade, A New Data Mining Based Network Intrusion Detection Model, Proc. of Intl. conference on Computer & communication technology, 2010, pp. 731-735.
  28. Mrutyunjaya Panda, Manas Ranjan Patra, Network Intrusion Detection Using Naïve Bayes, International Journal of Computer Science and Network Security, vol. 7 no. 12, 2007, pp. 258-262.
  29. Muda Z, Yassin W, Sulaiman M. N, Udzir N. I , Intrusion Detection based on k-means clustering and Naive Bayes classification, Proc. of 7th Intl. Conference on IT in Asia, 2011, pp. 1-6.
  30. Ohta S, R. Kurebayashi and K. Kobayashi. , Minimizing false positives of a decision tree classi?er for intrusion detection on the internet, Journal of Networks System Management, vol. 16, 2008, pp. 399–419. ISSN 1064-7570.
  31. Oyebode E. O, Fashoto S. G, Ojesanmi O. A, Makinde O. E, Intrusion Detection System for Computer Network Security, Australian Journal of Basic and Applied Sciences, vol. 5,no,12, 2011, pp. 1317-1320.
  32. Peddabachigari S, Abraham A, Grosan C, Thomas J, Modelling Intrusion Detection Systems Using Hybrid Intelligent Systems, Journal of Network and Computer Applications, vol. 30, 2007, pp. 114–132.
  33. Qing Song, Robust Support Vector Machine with Bullet Hole Image Classification, IEEE Transactions on Systems, Man, And Cybernetics—Part C: Applications And Reviews, Vol. 32, No. 4, 2002, pp. 440-448.
  34. Quinlan, C4. 5: Programs for Machine Learning, 1993, Morgan Kaufmann Publishers, San Mateo, CA.
  35. Sabhnani M, Serpen G(2003), Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context, In Proc. of the Intl. Conference on Machine Learning, Models, Technologies and Applications, vol. 1, pp. 209–215.
  36. Seiffert C, Taghi M. Khoshgoftaar, RUSBoost: A Hybrid Approach to Alleviating Class Imbalance, IEEE Transactions on Systems, Man, And Cybernetics—Part A: Systems And Humans, Vol. 40, No. 1, 2010, pp. 185-197.
  37. Shelly Xiaonan Wu, Wolfgang Banzhaf, The use of computational intelligence in intrusion detection systems: A review, Applied Soft Computing, 2010, pp. 1-35, Elsevier Publication
  38. Sheng Chen, Haibo He, Edwardo A. Garcia, RAMOBoost: Ranked Minority Oversampling in Boosting, IEEE Trans. On Neural Networks, vol. 21, no. 10, 2010, pp. 1624-1642.
  39. Srinivas Mukkamala, Guadalupe Janoski, Andrew Sung, Intrusion Detection: Support Vector Machines and Neural Networks, In Proceedings of the IEEE International Joint Conference on Neural Networks, 2002, pp. 1702-1707.
  40. Su-Yun Wu, Ester Yen, Data Mining-based intrusion detectors, Expert Systems with Applications, vol. 36, 2009, pp. 5605-5612, Elsevier.
  41. Taghi M. Khoshgoftaar, Jason van Hulse, Amri Napolitano, Comparing Boosting and bagging Techniques with Noisy and imbalanced data, IEEE Trans. On Systems, Man & Cybernetics-Part A: Systems and Humans, vol. 41, no. 3, 2011, pp. 552-568.
  42. Weiming Hu, Wei Hu, Steve Maybank, AdaBoost-Based Algorithm for Network Intrusion Detection, IEEE Transactions on Systems, Man, And Cybernetics—Part B: Cybernetics, vol. 38, no. 2, 2008, pp. 577-583.
  43. Wu S, Yen E, Data mining-based intrusion detectors, Expert Systems with Applications, vol. 36, no. 3, 2009, pp. 5605–5612.
  44. Zhang J, Zulkernine M, A Hybrid Network Intrusion Detection Technique Using Random Forests, Proc. of the First International Conference on Availability, Reliability and Security, 2006, pp. 262–269.
Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection classification KDDCup