期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2010
卷号：1
期号：4
页码：315-318
出版社：TechScience Publications
摘要：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
关键词：Feature selection; k-mean clustering; fuzzy k mean;clustering; J48 clustering; and KDDcup 99 dataset
