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Insider threat detection requires the identification of rare anomalies in contexts where evolving behaviors tend to mask such anomalies. This paper proposes and tests an ensemble-based stream mining algorithm based on supervised learning that addresses this challenge by maintaining an evolving collection of multiple models to classify dynamic data streams of unbounded length. The result is a classifier that exhibits substantially increased classification accuracy for real insider threat streams relative to traditional supervised learning (traditional SVM and one-class SVM) and other single-model approaches.
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