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Reseach Article

Network Intrusion Detection with Feature Selection Techniques using Machine-Learning Algorithms

by Koushal Kumar, Jaspreet Singh Batth
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 150 - Number 12
Year of Publication: 2016
Authors: Koushal Kumar, Jaspreet Singh Batth
10.5120/ijca2016910764

Koushal Kumar, Jaspreet Singh Batth . Network Intrusion Detection with Feature Selection Techniques using Machine-Learning Algorithms. International Journal of Computer Applications. 150, 12 ( Sep 2016), 1-13. DOI=10.5120/ijca2016910764

@article{ 10.5120/ijca2016910764,
author = { Koushal Kumar, Jaspreet Singh Batth },
title = { Network Intrusion Detection with Feature Selection Techniques using Machine-Learning Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2016 },
volume = { 150 },
number = { 12 },
month = { Sep },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume150/number12/26144-2016910764/ },
doi = { 10.5120/ijca2016910764 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:55:46.586956+05:30
%A Koushal Kumar
%A Jaspreet Singh Batth
%T Network Intrusion Detection with Feature Selection Techniques using Machine-Learning Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 150
%N 12
%P 1-13
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The task of developing Intrusion Detection System (IDS) crucially depends on the preprocessing along with selecting important data features of it. Another crucial factor is design of efficient learning algorithm that classify normal and anomalous patterns. The objective of this research work is to propose a new and better version of the Naive Bayes classifiers that improves the accuracy of intrusion detection in IDS. The proposed classifier is also supposed to take less time as compared with the existing classifiers. To gain better accuracy and fast processing of network traffic, this study applied three standard methods of feature selection. This study tested the performance of the new proposed classifier algorithm with existing classifiers, namely Naïve bayes, J48 and REPTree thereby measuring different performance parameters using 10-fold cross validation. This study evaluates the performance of the new proposed classifier algorithm by using NSL-KDD data set. Empirical results of our study show that the proposed updated version of the Naive Bayes classifiers gives better results in terms of intrusion detection and false alarm rate.

References
  1. Chih-Fong Tsai a, Yu-Feng Hsu b, Chia-Ying Lin c, Wei-Yang Lin d "Intrusion detection by machine learning A review" Expert Systems with Applications Elsevier 2009.
  2. Tanya Garg and Surinder Singh Khurana IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), May 09-11, 2014, Jaipur, India
  3. Jian Pei Shambhu J. Upadhyaya Faisal Farooq Venugopal Govindaraju. Proceedings of the 20thInternational Conference on Data Engineering published In IEEE 2004.
  4. Debar, H, Dacier, M., and Wespi, A, A Revised taxonomy for intrusion detection systems, Annales des Telecommunications Vol. 55, No.7–8, 361–378, 2000.
  5. Gulshan Kumar, Krishan Kumar & Monika Sachdeva (2010) “The use of artificial intelligence based techniques for intrusion detection: a review” Published online: 4 September 2010 © Springer Science+Business Media.
  6. Biesecker, Keith, Elizabeth Foreman, Kevin Jones and Barbara Staples (2008) “Intelligent Transportation Systems (ITS) Information Security Analysis.” United States Department of Transportation Technical Report FHWA-JPO-98-009, 16 November 2008.
  7. Siva S. Sivatha Sindhu, Geetha , A. Kannan ” Decision tree based light weight intrusion detection using a wrapper approach “.Expert Systems with Applications 39 (2012) 129–141 published in Elsevier
  8. Muamer N. Mohammada, Norrozila Sulaimana, Osama Abdulkarim Muhsin “A Novel Intrusion Detection System by using Intelligent Data Mining in Weka Environment”. Procedia Computer Science 3 (2011) 1237–1242
  9. F. Maggi, M. Matteucci and S. Zanero, “Reducing false positives in anomaly detectors through fuzzy alert aggregation”. Information Fusion, 10, 300–311. 2009
  10. C-C. Lin and M-S. Wang, “Genetic-clustering algorithm for intrusion detection system. International Journal of Information and Computer Security”, 2, 218–234. 2008
  11. Dr. Saurabh Mukherjee, Neelam Sharma, “Intrusion Detection using Naive Bayes Classifier with Feature Reduction” Published by Elsevier 2012.
  12. O. Y. Al-Jarrah1, A. Siddiqui1, M. Elsalamouny, P. D. Yoo1, S. Muhaidat1, K. Kim “Machine- Learning-Based Feature Selection Techniques for Large- Scale Network Intrusion Detection” 2014 IEEE 34th International Conference on Distributed Computing Systems Workshops.
  13. Heberlein, L. Todd, Dias, Gihan V, Levitt, Karl N, Mukherjee, Biswanath, Wood, Jeff, and Wolber, David, “A Network Security Monitor,” 1990 Symposium on Research in Security and Privacy, Oakland, CA, pages 296-304
  14. Paxson, Vern, Bro, “A System for Detecting Network Intruders in Real-Time,” Proceedings of The 7th USENIX Security Symposium, San Antonio TX, 1998.
  15. Mohammadreza Ektefa, Sara Memar, Fatimah Sidi, Lilly Suriani Affendey ,” Intrusion Detection Using Data Mining Techniques” 978-1-4244-5651-2/10/$26.00 ©2010 IEEE
  16. PAT LANGLEY, STEPHANIE SAGE,” Induction of Selective Bayesian Classifiers” Institute for the Study of Learning and Expertise 2451 High Street, Palo Alto, CA 94301
  17. ENGEN, “Machine learning for network based intrusion detection,” Doctoral dissertation, Bournemouth University, 2010.
  18. S. Zaman and F. Karray, “Features selection for intrusion detection systems based on support vector machines,” in Consumer Communications and Networking Conference, CCNC 2009. 6th IEEE, pp. 1–8, 2009.
  19. V. BoloN-Canedo, N. SaNchez-Marono, and A. Alonso-Betanzos, “Feature selection and classification in multiple class datasets: an application to kdd cup 99 dataset,” Expert Syst. Appl., vol. 38, pp. 5947–5957, 2011.
  20. T. O. Ayodele, "Types of Machine Learning Algorithms," in New Advances in Machine Learning, Y. Zhang, Ed., In Tech, 2010.
  21. Yang Y, Pedersen JO. A comparative study on feature selection in text categorization. Proceedings of the 14th International Conference on Machine Learning (ICML '97); 1997; Nashville, Tenn, USA. Morgan Kaufmann; pp. 412–420.
  22. Karan Bajaj, Amit Arora, “Dimension Reduction in Intrusion Detection Features Using Discriminative Machine Learning Approach” IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 4, No 1, July 2013
  23. I.H.Witten, E.Frank, M.A. Hall, “Data Mining Practical Machine Leanrning Tools & Techniques Third edition”, Morgan kouffman 2011.
  24. Sumaiya Thaseen, Ch. Aswani Kumar “An Analysis of Supervised Tree Based Classifiers for Intrusion Detection System” International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME) February 2013
  25. Mark A. Hall, “Correlation-based Feature Selection for Machine Learning” This thesis is submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy at The University of Waikato. April 1999
  26. Huy Anh Nguyen and Deokjai Choi “Application of Data Mining to Network Intrusion Detection: Classifier Selection Model” APNOMS 2008, LNCS 5297, pp. 399–408, © Springer-Verlag Berlin Heidelberg 2008
  27. Dr. Saurabh Mukherjee, Neelam Sharma “Intrusion Detection using Naive Bayes Classifier with Feature Reduction” Procedia Technology 4 (119 – 128). Published under Elsevier 2012.
  28. Yogendra Kumar Jain, Upendra “Intrusion Detection using Supervised Learning with Feature Set Reduction” International Journal of Computer Applications (0975 – 8887) Volume 33– No.6, November 2011.
  29. Gulshan Kumar and Krishan Kumar “Design of an Evolutionary Approach for Intrusion Detection” Hindawi Publishing Corporation The Scientific World Journal Volume 2013, Article ID 962185, 14 pages
  30. Jun Li1, Lixin Ding and Bo Li “A Novel Naive Bayes Classification Algorithm Based on Particle Swarm Optimization” The Open Automation and Control Systems Journal, 2014, 6, 747-753
  31. C. Kruegel, D.Mutz, W. Robertson, and F.Valeur, “Bayesian event classification for intrusion detection,” in Proceedings of the 19th Annual Computer Security Applications Conference (ACSAC 2003), 2003.
  32. Amor, Nahla B, Benferhat, S, Elouedi, Z. Naive Bayes vs. decision trees in intrusion detection systems. In: Proceedings of the 2004 ACM symposium on Applied computing, Cyprus, 2004, pp. 420–424.
  33. James P. Anderson, “Computer security threat monitoring and surveillance,” Technical Report 98-17, James P. Anderson Co., Fort Washington, Pennsylvania, USA, April 1980
  34. Dorothy E. Denning, “An intrusion detection Model,” IEEE Transaction on Software Engineering”, SE-13(2), 1987, pp. 222-232.
  35. T. S. Chou, K. K. Yen, and J. Luo “Network Intrusion Detection Design Using Feature Selection of Soft Computing Paradigms. International Journal of Computational Intelligence 2008.
  36. Dewan Md. Farid a, Li Zhang a, Chowdhury Mofizur Rahman b, M.A. Hossain a, Rebecca Strachan, “Hybrid decision tree and naive Bayes classifiers for multi-class classification tasks” Expert Systems with Applications Elsevier 2014.
  37. Mrutyunjaya Panda, Ajith Abraham, Manas Ranjan Patra “A Hybrid Intelligent Approach for Network Intrusion Detection” International Conference on Communication Technology and System Design 2011 Published by Elsevier.
  38. Saman M. Abdulla, Najla B. Al-Dabagh, Omar Zakaria, Identify Features and Parameters to Devise an Accurate Intrusion Detection System Using Artificial Neural Network, World Academy of Science, Engineering and Technology 2010.
  39. H Nguyen, K Franke, S Petrovic “Improving Effectiveness of Intrusion Detection by Correlation Feature Selection” , 2010 International Conference on Availability, Reliability and Security, IEEE Pages-17-24
  40. A Abraham, S Chebrolu, J P. Thomas “Feature deduction and ensemble design of intrusion detection systems” Computers & Security,Volume 24, Issue 4, June 2005, Pages 295-307
  41. Panda, Mrutyunjaya, Ajith Abraham, and Manas Ranjan Patra, “A Hybrid Intelligent Approach for Network Intrusion Detection,” International Conference on Communication Technology and System Design 2011,Procedia Engineering 30 (2012), 1-9.
  42. Saurabh, Mukherjee, and Neelam Sharma, “Intrusion detection using naive Bayes classifier with feature reduction,” Procedia Technology 4 (2012), 119-128 doi: 10.1016/j.protcy.2012.05.017
  43. Alhaddad, Mohammed, Amir Ahmed, Sami M. Halawani “A study of the modified KDD 99 dataset by using classifier ensembles approach,” IOSR Journal of Engineering, May. 2012, Vol. 2(5) pp: 961-965.
  44. S. Axelsson, "The base rate fallacy and its implications for the difficulty of Intrusion detection” Proc. Of 6th. ACM conference on computer and communication security 1999.
  45. Patricia E.N. Lutu, “Fast Feature Selection for Naive Bayes Classification in Data Stream Mining,” Proceedings of the World Congress on engineering, Vol III, WCE 2013.
  46. S. B. Kotsiantis, "Supervised Machine Learning: A Review of Classification," 2007.
  47. S Chebrolu, A Abraham, J P. Thomas Feature deduction and ensemble design of intrusion detection systems, Computers & Security,Volume 24, Issue 4, June 2005, Pages 295-307
  48. N. Ben Amor, S. Benferhat and Z. Elouedi. Naive Bayes vs. Decision Trees in Intrusion Detection Systems. In SAC ‟ 04: Proceedings of the 2004 ACM symposium on Applied computing, pages 420-424, New York, NY, USA, 2004. ACM. ISBN 1-58113-812-1.
  49. Xi-Zhao Wang, Yu-Lin He, Debby D. Wang, “Non-Naive Bayesian Classifiers for Classification Problems with Continuous Attributes” IEEE TRANSACTIONS ON CYBERNETICS 2013
  50. Sanoop Mallissery, Sucheta Kolekar, Raghavendra Ganiga “Accuracy Analysis of Machine Learning Algorithms for Intrusion Detection System using NSL-KDD Dataset” Future Trends in Computing and Communication 2013.
  51. You Chen, Yang Li, Xue-Qi Cheng, and Li Guo, “Survey and Taxonomy of Feature Selection Algorithms in Intrusion Detection System”, © Springer-Verlag Berlin Heidelberg 2006.
  52. Stańczyk U. Ranking of characteristic features in combined wrapper approaches to selection published in “Neural Computing and Applications”. 2015 Feb 1; 26(2):329–44.
  53. Datta H. Deshmukh, Tushar Ghorpade, Puja Padiya, Improving Classification using Preprocessing and Machine Learning Algorithms On NSL-KDD Dataset, 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Machine learning Intrusion Detection System (IDS) Naïve Bayes algorithm Feature selection NSL KDD data set