Feature Selection Using Relative Fuzzy Entropy and Ant Colony Optimization Applied to Real-time Intrusion Detection System

Intrusion Detection System (IDS) is one of the most important component of network defense mechanism. In an attempt to detect network attacks, network traffic features need to be identified and both attack and normal data need to be profiled. This paper proposes a set of network traffic features that can be extracted for Real-Time Intrusion Detection. This paper also proposes Fuzzy Entropy based heuristic for Ant Colony Optimization (ACO) in-order to search for global best smallest set of network traffic features for Real-Time Intrusion Detection Data set. The proposed feature reduction algorithm was tested on standard bench-mark UCI data sets, and found to be efficient. Further the algorithm was applied to Real-Time IDS data set and found to produce promising results.