Unsupervised Anomaly Based Network Intrusion Detection Using Farthest First and Hierarchical Conceptual Clustering.
作者： Mrutyunjaya Panda , Manas Ranjan Patra
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摘要: With the increased usage of computer networks, security becomes a critical issue. Recently, data mining methods have gained lot attention in addressing network issues, including intrusion detection. Consequently, unsupervised learning been given much importance for anomaly based In this paper, we investigate new clustering algorithms like farthest first and hierarchical conceptual (COBWEB) building our proposed detection model. We evaluated model using KDDCup’99 benchmark dataset. Our research shows that with five class classifications enable us to build an efficient high rate acceptable false positive comparison other existing detecting rare attacks.
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