Application of Feature Selection and Fuzzy ARTMAP to Intrusion Detection
Christina B. Vilakazi, Tshilidzi Marwala · 2006
This paper proposes a novel approach for intrusion detection and diagnosis. The proposed approach uses Sequential Backward Floating Search for feature selection and fuzzy ARTMAP for detection and diagnosis of attacks. The optimal vigilance parameter for the fuzzy ARTMAP is chosen using a genetic algorithm. The reduced set of features decreases the computation time by 0.789 s. A classification rate of 100% and 99.89% is obtained for the detection stage and diagnosis stage, respectively.