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Intrusion Detection System Methodologies Based on Data Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 5 - Number 2 |
| Year of Publication: 2010 |
| Authors: Dr.J.A.Chandulal, Dr.K.Nageswara Rao, Shaik Akbar |
10.5120/892-1266
|
Dr.J.A.Chandulal, Dr.K.Nageswara Rao, Shaik Akbar . Intrusion Detection System Methodologies Based on Data Analysis. International Journal of Computer Applications. 5, 2 ( August 2010), 10-20. DOI=10.5120/892-1266
Abstract
With the rapidly growing and wide spread use of computer networks the number of new threats has grown extensively. Intrusion and detection system can only identifying and protecting the attacks successfully. In this paper we focuses on detailed study of different types of attacks using in KDD99CUP Data Set and classification of IDS are also presented. They are Anomaly Detection System, Misuse Detection Systems. Different Data Analysis Methodologies also explained for IDS. To identify eleven data computing techniques associated with IDS are divided groups into categories. Some of those methods are based on computation such as Fuzzy logic and Bayesian networks, some are Artificial Intelligence such as Expert Systems, agents and neural networks some other are biological concepts such as Genetics and Immune systems.
References
- Anderson. J. P. “Computer Security Threat Monitoring and Surveillance.” Technical Report, James P Anderson Co., Fort Washington, Pennsylvania, 1980.
- Baiju Shah “How to Choose Intrusion Detection Solution” SANS Institute Resources, July 24, 2001.
- Danny Rozenblum “Understanding Intrusion Detection Systems” SANS Institute Resources, 2001.
- KDD Cup 1999 Data, Information and Computer Science,University of California, Irvine. http://kdd.ics.uci.edddatabases/kddcup99/kddcup99.html
- N. Srinivasan and V. Vaidehi. “Timed Coloured Petri Net Model for Misuse Intrusion Detection” First International Conference on Industrial and Information Systems, 8-11 Aug. 2006.
- Zirkle, L., “What is host-based intrusion detection? “Virginia Tech CNS. SANS Institute Resources, Intrusion Detection FAQ, Hyperlink ID FAQ, 2000.
- Northcutt, S. “What the Hackers Know about You. “SANS Institute. SANS Institute Resources, Intrusion Detection FAQ, Hyperlink: ID FAQ, 1999.
- Ong, T.H., C.P. Tan, Y.T. Tan, C.K. Chew, and C. Ting. “SNMS-Shadow Network Management System ”Proceedings of the Second International Workshop on Recent Advances in Intrusion Detection, W. Lafayette, IN, 1999.
- Brenda McAnderson & Paul Ramstedt, “Intrusion Detection Technology: Today and Tomorrow”, November, 18, 1999.
- [Denning 1987] An Intrusion-detection Model / Denning, D. E. – p. 118, IEEE Symposium on Security and Privacy, 1986.
- M. Mehdi, S. Zair, A. Anou and M. Bensebti “A Bayesian Networks in Intrusion Detection Systems” Journal of Computer Science 3 (5): 259-265, 2007, ISSN 1549-3636.
- Ryan, Meng-Jang Lin and Risto Miikulainen “Intrusion Detection with Neural Networks”, In Advances in Neural Information Processing Systems 10, Cambridge, MA: MIT Press, 1998.
- Susan C. Lee and David V. Heinbuch “Training a Neural-Network Based Intrusion Detector to Recognize Novel Attacks”, In IEEE Transactions on systems, man and cybernetics – part A: systems and humans, vol. 31, no. 4, July 2001.
- Anup K. Ghost and Aaron Schwartzbaard “A study in Using Neural Networks for Anomaly and Misuse Detection.” In Pp. 141-152 of Proceedings of the 8th USENIX Security Symposium, Washington D.C, August 23-26, 1999.
- Theodoros Lapps and Konstantinos Pelechrinis “Data Mining Techniques for (Network) Intrusion Detection Systems” Department of Computer Science and Engineering UC Riverside, Riverside CA 92521.
- Fan W., Miller M., Stolfo S., Lee W., Chan P “Using Artificial Anomalies to Detect Unknown and Known Network Intrusions”, In Proceedings of the First IEEE International Conference on Data Mining, San Jose, CA, November 2001.
- Lee W., Stolfo S, Mok K. “Adaptive Intrusion Detection: a Data Mining Approach”, Artificial Intelligence Review, 14(6), pp. 533-567, December 2000.
- Bass T “Intrusion Detection Systems Multisensor Data Fusion: Creating Cyberspace Situational Awareness” Communication of the ACM, Vol. 43, Number 1, pp. 99-105, January 2000.
- Yao, J. T., S.L. Zhao, and L.V. Saxton, “ A study on fuzzy intrusion detection ”, Proceedings of SPIE Vol. 5812, Data Mining, Intrusion Detection, Information Assurance, And Data Networks Security, , Orlando, Florida, USA .,28 March - 1 April 2005.
- Gomez, J., and D. Dasgupta. "Evolving Fuzzy Classifiers for Intrusion Detection.", Proceedings of the 2002 IEEE, Workshop on Information Assurance, United States Military Academy, West Point, NY., June 2001.
- Bobor, V. "Efficient Intrusion Detection System Architecture Based on Neural Networks and Genetic Algorithms.", Department of Computer and Systems Sciences, Stockholm University / Royal Institute of Technology, KTH/DSV, 2006.
- R. Sekar, A. Gupta, J. Frullo, T. Hanbhag, A. Tiwari, H. Yang, and S. Zhou, “Specification-Based Anomaly Detection: a New Approach for Detecting”, International Journal of Network Security, Vol. 1, No.2, pp. 84–102, 2005.
- T. Peng, C. Leckie and K. Ramamohanarao, “Information Sharing for Distributed Intrusion Detection Systems”, Journal of Network and Computer Applications, Vol. [MAT02], No. 3, pp. 877-899, 2007.
- A. Pagnoni, and A. Visconti “An Innate Immune System for the Protection of Computer Networks”, ACM International Conference Proceeding Series, Vol. 92 archive Proceedings of the 4th international symposium on Information and communication technologies, 2005.
- F.Sabahi, IEEE Member, A.Movaghar, IEEE Senior Member “Intrusion Detection: A Survey” The Third International Conference on Systems and Networks Communications, IEEE, 2008.
- Theuns Verwoerd and Ray Hunt, “Intrusion Detection Techniques and Approaches”, Theuns Verwoerd and Ray Hunt, Department of Computer Science, University of Canterbury, New Zealand.
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