Fuzzy Logic and Genetic Algorithm for Data Mining based Intrusion Detection System: A Review Approach

Abstract

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).