Open Access
Subscription Access
Intrusion Detection System Based on Data Mining Techniques
Network security is one of the most important non-functional requirements in a system. Over the years, many software solutions have been developed to enhance network security. Intrusion Detection System (IDS) we have provided an overview of different types of intrusion Detection Systems, the advantages and disadvantages of the same. The need for IDS in a system environment and the generic blocks in IDS is also mentioned.The examples are as follows: (1) Misuse intrusion detection system that uses state transition analysis approach, (2) Anomaly based system that uses payload modeling and (3) Hybrid model that combines the best practices of Misuse and Anomaly based intrusion systems.
Keywords
- Aurobindo Sundaram, 1996 An Introduction to Intrusion Detection, Crossroads, Volume 2, Issue 4, Pages: 3 – 7,
- C. Platt. Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. A. Smola, P. Bartlett, B. Scholkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, Cambridge, MA, 2000. MIT Press.
- Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin(2003), A Practical Guide to Support Vector Classification Department of Computer Science National Taiwan University, Taipei 106, Taiwan.
- Chunhua Gu and Xueqin Zhang (2009)” A Rough Set and SVM Based Intrusion Detection Classifier”, Second International Workshop on Computer Science and Engineering. http://ilta.ebiz.uapps.net/ ProductFiles/productfiles/672/wireshark.ppt
- James P. Anderson(April 1980)”Computer Security Threat Monitoring and Surveillance” Technical report, James P. Anderson Co., Fort Washington, Pennsylvania.
- MrutyunjayaPanda1, Manas RanjanPatra ( May 2009) ‘’Evaluating Machine Learning Algorithms for Detecting Network Intrusions’’, Internation- al Journal of Recent Trends in Engineering, Vol. 1, No. 1
- R. Heady, G. Luger, A. Maccabe, and M. Servilla (August 1990) The Architecture of a Network Level Intrusion Detection System. Technical report, Department of Computer Science, University of New Mexico,.
- RafatRana S.H. Rizvi A Review on Intrusion Detection System Professor Computer Science and Engineering H.V.P.M’s C.O.E.T Amravati, India Computer Science and Engineering H.V.P.M’s C.O.E.T Amravati, India
- Ranjit R Keole A Review on Intrusion Detection System Professor Information Technology India Computer Science and Engineering H.V.P.M’s C.O.E.T Amravati, India
- Sandeep Kumar( August 1995) Classification and Detection of Computer Intrusions. Ph.D. Dissertation,.
- Upendra,( 2013)’’An Efficient Feature Reduction Comparison of Machine Learning Algorithms for Intrusion Detection System’’, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 2, Issue 1, January – February.
- Wenke Lee and Salvatore J. Stolfo, (Nov 2000) “A Framework for constructing features and models for intrusion detection systems”,ACM transactions on Information and system security (TISSEC), vol.3, Issue 4.
- Y. Hu, B. Panda, “A Data Mining Approach for Database Intrusion Detection”, Proceedings of the ACM Symposium on Applied Computing, pp. 711-716 (2004)
- Yogita B. Bhavsar, Kalyani C.Waghmare March 2013 Intrusion Detection System Using Data Mining Technique www.ijetae.com Support Vector Machine
- Yogita B.Bhavsar. Intrusion Detection System Using Data Mining Technique: Support Vector
Abstract Views: 198
PDF Views: 0