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Intrusion detection systems constitute a crucial cornerstone in securing computer networks especially after the recent advancements in attacking techniques. IDSes can be categorized according to the nature of detection into two major categories: signature-based and anomaly-based. In this paper, the authors present KBIDS, a kernel-based method for an anomaly-based IDS that tries to cluster the training data to be able to classify the test data correctly. The method depends on the K-Means algorithm that is used for clustering. Their experiments show that the accuracy of detection of KBIDS increases exponentially with the number of clusters.
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