Comparative Analysis of Intrusion Detection System with Mining

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Intrusion Detection System (IDS) is a system that monitors the network activities for a suspicious event. Suspiciousness in the event cannot be identified by a single activity. Set of activities that crosses the limit of normal behavior is considered as an intrusion. There are many methods that provide security like authorization, authentication, and encryption. But all these methods decide the security issue by a single activity so that the slow intrusion is not identified. In this paper, we review the existing real time IDS models that capture the slow poisoning of the network by using improved data mining algorithms. Normal IDS has the issues like low accuracy, high false negative rates. This system suggests the high accuracy IDS system with less false positives.
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Anomaly based Intrusion Detection; Decision Tree; Intrusion Detection System (IDS); KDD Cup 99 Data Set; Misuse Detection; SVM

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