Fuzzy K-Mean Clustering Via J48 For Intrusion Detection System
Due to fast growth of the internet technology there is need to establish security mechanism. So for achieving this objective NIDS is used. Datamining is one of the most effective techniques used for intrusion detection. This paper evaluates the performance of unsupervised learning techniques over benchmark intrusion detection datasets. The model generation is computation intensive, hence to reduce the time required for model generation various feature selection algorithm. Various algorithms for cluster to class mapping have been proposed to overcome problem like, class dominance, and null class problems.