Modern intrusion detection applications face complex requirements - they need to be reliable, extensible, easy to manage, and have low maintenance cost. In recent years, data mining-based Intrusion Detection Systems (IDSs) have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. Still, significant challenges exist in the design and implementation of production quality IDSs. Instrumenting components such as data transformations, model deployment, and cooperative distributed detection remain a labor intensive and complex engineering endeavor. This paper describes Database centric Architecture for Intrusion Detection (DAID), a database-centric architecture that leverages data mining within the Oracle RDBMS to address these challenges.
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