  Insider threats, as one type of the most challenging threats in cyberspace,
usually cause significant loss to organizations. While the problem of insider
threat detection has been studied for a long time in both security and data
mining communities, the traditional machine learning based detection
approaches, which heavily rely on feature engineering, are hard to accurately
capture the behavior difference between insiders and normal users due to
various challenges related to the characteristics of underlying data, such as
high-dimensionality, complexity, heterogeneity, sparsity, lack of labeled
insider threats, and the subtle and adaptive nature of insider threats.
Advanced deep learning techniques provide a new paradigm to learn end-to-end
models from complex data. In this brief survey, we first introduce one
commonly-used dataset for insider threat detection and review the recent
literature about deep learning for such research. The existing studies show
that compared with traditional machine learning algorithms, deep learning
models can improve the performance of insider threat detection. However,
applying deep learning to further advance the insider threat detection task
still faces several limitations, such as lack of labeled data, adaptive
attacks. We then discuss such challenges and suggest future research directions
that have the potential to address challenges and further boost the performance
of deep learning for insider threat detection.
