There is a growing consensus within the intelligence community that malicious insiders are perhaps the most potent threats to information assurance in many or most organizations ([BRAC04], [HAMP99], [MATZ04], [SALE11]). One traditional approach to the insider threat detection problem is supervised learning, which builds data classification models from training data. Unfortunately, the training process for supervised learning methods tends to be time-consuming and expensive, and generally requires large amounts of well-balanced training data to be effective. In our experiments, we observe that <3% of the data in realistic datasets for this problem is associated with insider ...
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