Along with the modernization of technological era, the technological advancement has also raised concerns about the security of web activities. These activities are in a way or the other are attempted to be compromised by the adversary with the aim of gaining knowledge which may be somehow useful for him/her. In addition, terrorists are also utilizing web for fulfilling their inhuman goals which is currently an utmost concern for security agencies. Although there are many successful attempts have been made to restrict the existence of these illegitimate people, there still is a need for an effective affirmation solution. In respect to this, data mining comes out as a solution by bringing into existence a mining concept named Terrorist Network Mining. Terrorist network mining has proved as the most feasible solution where detection and analysis of terrorists is well performed. Still there were some improvements required to this concept which was efficiently done by combining fuzzy with genetic algorithms with the intrusion detection system (IDS) resulting into significant and efficient detection process. Hence the paper discusses about how well an intrusion detection system performs when combined with fuzzy data mining (reveal patterns whose behavior is intrusive) with genetic algorithm (leads to the success of efficient detection of intruders).

Apriority algorithm, Data mining, Fuzzy logic, Genetic algorithm

[1]. Y.Dhanalakshmi and Dr.I. Ramesh Babu, “Intrusion Detection Using Data Mining Along Fuzzy Logic and Genetic Algorithms”, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.2,February 2008.
[2]. German Florez,Susan M. Bridges, and Rayford B. Vaughn, “An Improved Algorithm for Fuzzy Data Mining for Intrusion Detection”, Proceedings of Information Processing Society, 2002, Page(s): 457 – 462.
[3]. Jiawei Han & Micheline Kamber (2006) Data Mining;Concepts and Techniques, Second Edition, Morgan Kaufmann Publishers.
[4]. Ci Chen, Shingo Mabu, Chuan Yue, Kaoru Shimada, and Kotaro Hirasawa, “Network Intrusion Detection using Fuzzy Class Association Rule Mining Based on Genetic Network Programming”, Proceedings of the 2009 IEEE International Conference on Systems, October 2009.
[5]. Shingo Mabu,, Ci Chen, Nannan Lu, Kaoru Shimada, and Kotaro Hirasawa, “An Intrusion-Detection Model Based on Fuzzy Class-Association-Rule Mining Using Genetic Network Programming”, Proceedings of IEEE transactions on systems, man, and cybernetics—part c: applications and reviews, vol. 41, no. 1, January 2011.
[6]. Tan Jun-shan, He Wei1, Qing Yan ,” Application of Genetic Algorithm in Data Mining”. 2009 First Int. Workshop on Education Technology and Computer Science. 978-0-7695-3557-9/09 © 2009 IEEE. DOI10.1109/ETCS.2009.340. page 353.page 353.