期刊名称：International Journal on Computer Science and Engineering
印刷版ISSN：2229-5631
电子版ISSN：0975-3397
出版年度：2010
卷号：2
期号：6
页码：2197-2200
出版社：Engg Journals Publications
摘要：Due to continuous growth of the internet technology, there is need to establish security mechanism. So for achieving this objective various NIDS has been propsed. 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 has been used. Problems with k-mean clustering are hard cluster to class assignment, 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 statistical model is giving better performance better results than existing clustering algorithm.
关键词：Feature selection; k-mean clustering; fuzzy k mean clustering; Random Forest; and KDDcup 99 dataset
