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Reseach Article

Survey on Intrusion Detection System using Machine Learning Techniques

by Sharmila Kishor Wagh, Vinod K. Pachghare, Satish R. Kolhe
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 78 - Number 16
Year of Publication: 2013
Authors: Sharmila Kishor Wagh, Vinod K. Pachghare, Satish R. Kolhe
10.5120/13608-1412

Sharmila Kishor Wagh, Vinod K. Pachghare, Satish R. Kolhe . Survey on Intrusion Detection System using Machine Learning Techniques. International Journal of Computer Applications. 78, 16 ( September 2013), 30-37. DOI=10.5120/13608-1412

@article{ 10.5120/13608-1412,
author = { Sharmila Kishor Wagh, Vinod K. Pachghare, Satish R. Kolhe },
title = { Survey on Intrusion Detection System using Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 78 },
number = { 16 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 30-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume78/number16/13608-1412/ },
doi = { 10.5120/13608-1412 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:51:45.031518+05:30
%A Sharmila Kishor Wagh
%A Vinod K. Pachghare
%A Satish R. Kolhe
%T Survey on Intrusion Detection System using Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 78
%N 16
%P 30-37
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In today's world, almost everybody is affluent with computers and network based technology is growing by leaps and bounds. So, network security has become very important, rather an inevitable part of computer system. An Intrusion Detection System (IDS) is designed to detect system attacks and classify system activities into normal and abnormal form. Machine learning techniques have been applied to intrusion detection systems which have an important role in detecting Intrusions. This paper reviews different machine approaches for Intrusion detection system. This paper also presents the system design of an Intrusion detection system to reduce false alarm rate and improve accuracy to detect intrusion.

References
  1. S. Chebrolu, A. Abraham, and J. P. Thomas, "Feature deduction and ensemble design of intrusion detection systems," Comput. Secure. , vol. 24, no. 4, pp. 295–307, Jun. 2005
  2. W. Lee and S. J. Stolfo, "A framework for constructing features and models for intrusion detection systems," ACM Trans. Inf. Syst. Secur. vol. 3, no. 4, pp. 227–261, Nov. 2000.
  3. Denning D, "An Intrusion-Detection Model," IEEE Transactions on Software Engineering, Vol. SE-13, No 2, Feb 1987.
  4. Lazarevic A, Kumar V, Srivastava J. Intrusion detection: "A survey, Managing cyber threats: issues, approaches, and challenges," Springer Verlag; 2005. pp. 330.
  5. Denning DE, Neumann PG. "Requirements and model for IDES – a real-time intrusion detection system," Computer Science Laboratory, SRI International; 1985. Technical Report #83F83- 01-00
  6. Anderson D, Lunt TF, Javitz H, Tamaru A, Valdes A. "Detecting unusual program behavior using the statistical component of the next-generation intrusion detection expert system (NIDES)," Menlo Park, CA, USA: Computer Science Laboratory, SRI International; 1995. SRIO-CSL-95-06.
  7. Ye N, Emran SM, Chen Q, Vilbert S. " Multivariate statistical analysis of audit trails for host-based intrusion detection," IEEE Transactions on Computers 2002;51(7).
  8. Wenke Lee and Salvatore J. Stolfo, "A framework for constructing features and models for intrusion detection systems," 2000, ACM Trans. Inf. Syst. Secur. , 3(4):227–261.
  9. Heckerman D. "A tutorial on learning with Bayesian networks," Microsoft Research; 1995. Technical Report MSRTR-95-06.
  10. Bridges, Vaughn, "Fuzzy Data mining and genetic algorithms applied to intrusion detection," In: Proceedings of the National Information Systems Security Conference; 2000. pp. 13–31.
  11. Li W. "Using genetic algorithm for network intrusion detection," C. S. G. Department of Energy; 2004. pp. 1–8.
  12. Y. Zhai, P. Ning, P. Iyer, D. S. Reeves, "Reasoning about complementary intrusion evidence," in: Proceedings of the 20th Annual Computer Security Applications Conference (ACSAC 04), December 2004.
  13. X. D. Hoang, J. Hu, P. Bertok, "A program-based anomaly intrusion detection scheme using multiple detection engines and fuzzy inference," Journal of Net- work and Computer Applications 32 (2009) 1219–1228.
  14. Kamra, Bertino, "Design and Implementation of an Intrusion Response System for Relational Databases," IEEE Transaction on Knowledge and Data Engineering, Volume: 23, Issue: 6 doi 10. 1109 /TKDE. 2010. 151 ,2011, pp: 875 – 888
  15. Suhail Owais ,Václav Snášel, Pavel Krömer,Ajith Abraham ,"Survey: Using Genetic Algorithm Approach in Intrusion Detection Systems Techniques ",978-0-7695-3184-7/08 DOI 10. 1109/CISIM7th Computer Information Systems and Industrial Management Applications. /2008 IEEE
  16. C. Xiang and S. M. Lim, "Design of multiple-level hybrid classifier for intrusion detection system," in Workshop on Machine Learning for Signal Processing, 2005, pp. 117–122.
  17. B. Daniel, C. Julia, J. Sushil, P. Leonard, N. N. Wu, "ADAM: Detecting intrusions by data mining", Proceedings of the 2001 IEEE, workshop on Information Assurance and Security, West Point, NY, 2001.
  18. Murali A, Rao M, "A Survey on Intrusion Detection Approaches," Information and Communication Technologies, 2005. ICICT 2005. First International Conference on DOI: 10. 1109/ICICT. 2005. 1598592, Year: 2005, pp: 233 – 240
  19. Mrutyunjaya Panda, and Manas Ranjan Patra " NETWORK INTRUSION DETECTION USING NAÏVE BAYES ", IJCSNS International Journal of Computer Science and Network Security, VOL. 7 No. 12, December 2007
  20. Li Xiangmei Qin Zhi "The Application of Hybrid Neural Network Algorithms in Intrusion Detection System "978-1-4244-8694-6/11 ©2011 IEEE
  21. Xiangmei Li ,"Optimization of the Neural-Network-Based Multiple Classifiers Intrusion Detection System ",978-1-4244-5143-2/10 ©2010 IEEE
  22. Naeem Seliya Taghi M. Khoshgoftaar, "Active Learning with Neural Networks for Intrusion Detection", IEEE IRI 2010, August 4-6, 2010, Las Vegas, Nevada, USA 978-1-4244-8099-9/10
  23. H. H. Hosmer, Security is fuzzy!: applying the fuzzy logic paradigm to the multipolicy paradigm, Proceedings of the 1992-1993 workshop on New security paradigms, ACM New York, NY, USA, 1993, pp. 175-184.
  24. John E. Dickerson and Julie A. Dickerson, Fuzzy network profiling for intrusion detection, Proceedings of NAFIPS 19th International Conference of the North American Fuzzy Infor mation Processing Society (Atlanta, USA), July 2000, pp. 301-306.
  25. T. Lunt and I. Traore, Unsupervised Anomaly Detection Using an Evolutionary Extension of K-means Algorithm,International Journal on Information and computer Science, Inderscience Pulisher 2 (May, 2008), 107-139.
  26. Jiankun Hu, Xinghuo Yu, Qiu D, Hsiao-Hwa Chen; "A simple and efficient hidden Markov model scheme for host-based anomaly intrusion detection," IEEE Transaction on Network, Volume: 23, Issue: 1 DOI: 10. 1109/MNET. 2009. 4804323, Year: 2009, Page(s): 42 – 47.
Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection System (IDS) Machine Learning Techniques Anomaly Detection False Alarm Rate (FAR).