The week's pick
Reseach Article
Using Machine Learning and Statistical Models for Intrusion Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 31 |
| Year of Publication: 2020 |
| Authors: Kamini C. Nalavade |
10.5120/ijca2020920854
|
Kamini C. Nalavade . Using Machine Learning and Statistical Models for Intrusion Detection. International Journal of Computer Applications. 175, 31 ( Nov 2020), 14-21. DOI=10.5120/ijca2020920854
Abstract
Detecting intrusions and preventing the possible attacks is a critical aspect of computer based system security. Efforts have been made to achieve this goal such as firewalls, intrusion detection system, anti-virus, organizational security policies and many more. In this paper research work in developing general and systematic method for intrusion detection and prevention systems is discussed. This paper focuses on literature survey carried out for building efficient intrusion detection and prevention system. Previous research and applied methodologies of intrusion detection are reviewed and studied. The Denning's model and the statistical approaches for intrusion detection are described. After the comprehensive study and survey of previous work on intrusion detection systems, here we propose a model for intrusion detection and prevention using machine learning.
References
- Paul Innella and Oba McMillan, Tetrad Digital Integrity, LLC “An Introduction to Intrusion Detection Systems” December 6, 2001
- Micheal E. Whitman and Herbert J. Mattord, “Principles of Information Security” page 289-294
- Karthikeyan .K.R1 and A. Indra, “Intrusion Detection Tools and Techniques –A Survey”, International Journal of Computer Theory and Engineering, Vol 2 , No. 6, December 2010. 1793-8201
- Kusum Kumari Bharti , Sanyam Shukla , Sweta Jain, “Intrusion detection using clustering”, Special Issue of IJCCT Vol.1 Issue 2, 3, 4; 2010 for International Conference [ACCTA-2010], 3-5 August 2010
- http://kdd.ics.uci.edu/databases/kddcup99/task.html
- W. Lee, S.J.Stolfo et al, ”A data mining and CIDF based approach for detecting novel and distributed intrusions”, Proc. of Third International Workshop on Recent Advancesin Intrusion Detection (RAID 2000), Toulouse, France.
- Yeung, D.-Y. and C. Chow, ”Parzen-window network intrusion detectors”, In Proc. of the Sixteenth International Conference on Pattern Recognition, Volume 4, Quebec City, Canada, pp. 385388. IEEE Computer Society, 11-15 August, 2002.
- Carbone, P. L., ”Data mining or knowledge discovery in databases: An overview”, In Data Management Handbook. New York: Auerbach Publications, 1997.
- Ourston et al., ”Applications of Hidden Markov Models to Detecting Multi-stage Network Attacks”, Proceedings of the 36th Hawaii International Conference on System Sciences (HICSS03).
- Lane, T. D., ”Machine Learning Techniques for the computer security domain of anomaly detection”, Ph. D. thesis, Purdue Univ., West Lafayette, IN, August, 2000.
- Warrender, C., S. Forrest, and B. A. Pearlmutter, ”Detecting intrusions using system calls: Alternative data models”, In Proc. of the 1999 IEEE Symp. on Security and Privacy, Oakland, CA, pp. 133145. IEEE Computer Society Press, 1999.
- Theodoros Lappas and Konstantinos Pelechrinis “Data Mining Techniques for (Network) Intrusion Detection Systems”, Department of Computer Science and Engineering UC Riverside, Riverside CA 92521
- Lenoid portnoy, Eleazar Eskin and Sal Stolfo, “Intrusion detection with unlabeled data using clustering”, Department of Computer Scinece, Columbia University
- S. Selvakani Kandeeban Dr. R.S. Rajesh, “A Genetic Algorithm Based elucidation for improving Intrusion Detection through condensed feature set by KDD 99 data set”, Information and Knowledge Management www.iiste.org ISSN 2224-5758 (Paper) ISSN 2224-896X (Online) Vol 1, No.1, 2011
- Pedro A. Diaz-Gomez and Dean F. Hougen, “ Three Approaches to Intrusion Detection. Analysis and Enhancements” , National Computer and Information Security Conference ACIS 2006 – colombia
- Ramana Rao Kompella, Sumeet Singh, George Varghese, “On Scalable Attack Detection in the Network”. IEEE /ACM Transactions on Networking, Vol. 15, No. 1, February 2007, Student Member, IEEE and Member IEEE.
- Moses Garuba, Chunmei Liu, and Duane Fraites. “Intrusion Techniques: Comparative Study of Network Intrusion Detection Systems”. Fifth International Conference on Information Technology: New Generations. 978-0-7695-3099-4/08 $25.00 © 2008 IEEE.Department of Systems and Computer Science, Howard University
- Juan D. Penpgosl, Nagarajan Prabhakaran2, Subbarao V. Wunnava. “An Efficient Scheme for Dynamic Signature Verification”. 1996 IEEE. Departme3t of Electrical & Computer Engineering, School of Computer Science.
- Ricardo Koller, Raju Rangaswami, Joseph Marrero, Igor Hernandez, Geoffrey Smith. “Anatomy of a Real-time Intrusion Prevention System”. International Conference on Automonic Computing. 978-0-7695-3175-5 2008. School of Computing and Information Sciences, Florida International University. FL 33 1996.
- Teenam Bansode, B.B.Meshram, “Hybrid Intrusion Prevention System for End Users”, VIT Conference 2008-09
- Yijie Han, Sujaa Rani Mohan, E.K. Park, “An Adaptive Intrusion Detection System Using Data Mining Aproach” , University of Missouri, Kansas City
- Kamini Nalavade, BB Meshram, “Data Classification Using Support Vector Machine”, National Conference on Emerging Trends in Engineering & Technology (VNCET) 2012.
- Kamini Nalavade, BB Meshram, “Evaluation of K-Means Clustering for Effective Intrusion Detection and Prevention in Massive Network Traffic Data” International Journal of Computer Applications, Vol97, Issue 62014.
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