Unsupervised User-Based Insider Threat Detection Using Bayesian Gaussian Mixture Models. (arXiv:2211.14437v1 [cs.CR])

Insider threats are a growing concern for organizations due to the amount of
damage that their members can inflict by combining their privileged access and
domain knowledge. Nonetheless, the detection of such threats is challenging,
precisely because of the ability of the authorized personnel to easily conduct
malicious actions and because of the immense size and diversity of audit data
produced by organizations in which the few malicious footprints are hidden. In
this paper, we propose an unsupervised insider threat …

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