Intrusion Detection System Using Fuzzy Inference System

International Journal of Computer & Organization Trends  (IJCOT)          
© 2013 by IJCOT Journal
Volume-3 Issue-4                           
Year of Publication :  2013
Authors : Harikishan ,P.Srinivasulu


Harikishan, P.Srinivasulu "Intrusion Detection System Using Fuzzy Inference System" . International Journal of Computer & organization Trends (IJCOT), V3(4):59-66 Jul - Aug 2013, ISSN 2249-2593, Published by Seventh Sense Research Group.

Abstract—Network security consists of the provisions and policies adopted by a network administrator to prevent and monitor unauthorized access or denial of a computer network and network accessible resources. Intrusions are the activities that violate the security policy of a system. An Intrusion detection system monitors network or system activities for malicious activities or policy violations and produces reports to a management station. Previously several machine learning algorithms such as neural network ,data mining and many more have been used to detect intrusion behaviour. In the proposed system we have designed fuzzy inference approach for effectively identifying the intrusion activities within a network. The proposed system uses Sugeno Fuzzy Inference system for generation of fuzzy rules and ANFIS editor for experimentation. The experimentation and evaluation of the proposed intrusion detection system are performed with KDDCup99Dataset and we can easily detect whether the records are normal or attack one.


[1] Zadeh, L.A., "Outline of a new approach to the analysis of complex systems and decision processes," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 3, No. 1, pp. 28-44, Jan. 1973.
[2] Susan M. Bridges and Rayford B.Vaughn, “Fuzzy Data Mining And Genetic Algorithms Applied To Intrusion Detection”, In Proceedings of the National Information Systems Security Conference (NISSC), Baltimore, MD, pp.16-19, October 2000
[3].Network intrusion detection system using fuzzy logic Shanmugavadivu Indian Journal of Computer Science and Engineering01/2011
[4]. Intrusion Detection Techniques. Peng Ning, North Carolina State University. Sushil Jajodia, George Mason University. Introduction. Anomaly Detection
[5] J. H. Güneş Kayacýk, A. Nur Zincir-Heywood, Malcolm I. Heywood. Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets
[6.] Knowledge discovery in databases DARPA archive and Task Description. .
[7].Qiang Wang and Vasileios Megalooikonomou, "A clustering algorithm for intrusion detection", in Proceedings of theconference on Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security, vol. 5812, pp.31-38, March 2005.
[8].Combined Feature Selection and classification – A novel approach for the categorization of web pages K. Selvakuberan, M. Indradevi, Dr. R. Rajaram Innovation Lab.
[9]‎ Available: ... .edu/~fishwick/paper/paper.html.
[11] Discriminant Analysis based Feature Selection in KDD Intrusion Dataset. By Dr.S.Siva Sathya, Dr. R.Geetha Ramani and K.Sivaselvi
[12]. Designing Fuzzy Inference Systems from Data:An Interpretability-Oriented Review Serge Guillaume
[13]. A Detailed Analysis of the KDD CUP 99 Data Set Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu, and Ali A. Ghorbani
[14] T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Tran. Syst., Man, Cybern., vol. SMC 15, pp. 116–132, 1985.
[15]. A Database of Computer Attacks for the Evaluation of Intrusion Detection Systems by kristopher kendell,d.bmk
[16] E. H. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller,” Int. J. Man-Mach. Stud., vol. 7, pp. 1–13, 1975.

Keywords— IDS,KDDCup99Dataset,FuzzyInferenceSystem,Anfis editor.