Fuzzy K-mean Clustering Via Random Forest For Intrusiion Detection System
Journal Title: International Journal on Computer Science and Engineering - Year 2010, Vol 2, Issue 6
Abstract
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 election algorithm has been used. Problems with k-mean clustering are hard cluster to class assignment, class ominance, 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% recision and 100% recall, So it can be summarize that roposed statistical model is iving better performance better results than existing clustering lgorithm.
Authors and Affiliations
Kusum bharti , Shweta Jain , Sanyam Shukla
Keywords
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