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As network attacks have increased in number and severity over the past few years, Intrusion Detection System (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem that is receiving more and more attention from the research community. The uncertainty to explore if certain algorithms perform better for certain attack classes constitutes the motivation for the reported herein. In this paper, the authors evaluate performance of a comprehensive set of classifier algorithms using KDD99 dataset.
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