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This paper presents an overview of the research in real time data mining-based Intrusion Detection Systems (IDSs). It focuses on issues related to deploying a data mining-based IDS in a real time environment. The paper describes the approaches to address three types of issues: accuracy, efficiency, and usability. To improve accuracy, data mining programs are used to analyze audit data and extract features that can distinguish normal activities from intrusions; it use artificial anomalies along with normal and/or intrusion data to produce more effective misuse and anomaly detection models. To improve efficiency, the computational costs of features are analyzed and a multiple-model cost-based approach is used to produce detection models with low cost and high accuracy.
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