Fuzzy K-mean Clustering Via J48 For Intrusiion Detection System
Journal Title: International Journal of Computer Science and Information Technologies - Year 2010, Vol 1, Issue 4
Abstract
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 work 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. From experimental results it is observed that for 2 class datasets filtered fuzzy random forest dataset gives the better results. It is having 99.2% precision and 100% recall, So it can be summarize that proposed percentage is assignments and statistical model is giving better performance.
Authors and Affiliations
Kusum Bharti , Shweta Jain , Sanyam Shukla
Keywords
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