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Over the years social network data has been mined to predict individuals' traits such as intelligence and sexual orientation. While mining social network data can provide many beneficial services to the user such as personalized experiences, it can also harm the user when used in making critical decisions such as employment. In this work, we investigate the reliability of applying data mining techniques...
Insider threats are veritable needles within the haystack. Their occurrence is rare and when they do occur, are usually masked well within normal operation. The detection of these threats requires identifying these rare anomalous needles in a contextualized setting where behaviors are constantly evolving over time. To this refined search, this paper proposes and tests an unsupervised, ensemble based...
We propose a novel class-based micro-classifier ensemble classification technique (MCE) for classifying data streams. Traditional ensemble-based data stream classification techniques build a classification model from each data chunk and keep an ensemble of such models. Due to the fixed length of the ensemble, when a new model is trained, one existing model is discarded. This creates several problems...
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...
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