# Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities

**Authors:** Shuhan Yuan, Xintao Wu  
**arXiv:** [2005.12433](https://arxiv.org/abs/2005.12433)  
**Date:** 2020-05-25  
**Summary page:** https://www.summarizepaper.com/en/paper/2005.12433v1/

## Key points

- Insider threats pose significant challenges in cyberspace and can result in substantial losses for organizations.
- Traditional machine learning approaches struggle to accurately capture the behavioral differences between insiders and normal users due to various challenges associated with the underlying data.
- Advanced deep learning techniques offer a new paradigm for learning end-to-end models from complex data.
- Deep learning models can enhance the performance of insider threat detection compared to traditional machine learning algorithms.
- Applying deep learning techniques to further advance insider threat detection still faces limitations such as a lack of labeled data and adaptive attacks.
- The authors propose future research directions that have the potential to overcome these obstacles and improve the performance of deep learning for insider threat detection.

## Summary

Insider threats pose significant challenges in cyberspace and can result in substantial losses for organizations. Detecting insider threats has been a topic of study in the security and data mining communities for a considerable amount of time. However, traditional machine learning approaches that heavily rely on feature engineering struggle to accurately capture the behavioral differences between insiders and normal users due to various challenges associated with the underlying data. These challenges include high-dimensionality, complexity, heterogeneity, sparsity, lack of labeled insider threats, and the subtle and adaptive nature of insider threats. To address these limitations, advanced deep learning techniques offer a new paradigm for learning end-to-end models from complex data. In this brief survey by Shuhan Yuan and Xintao Wu titled "Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities," they introduce a commonly-used dataset for insider threat detection and review recent literature on deep learning approaches in this field. The existing studies demonstrate that compared to traditional machine learning algorithms, deep learning models can enhance the performance of insider threat detection. However, applying deep learning techniques to further advance insider threat detection still faces certain limitations such as a lack of labeled data and adaptive attacks. The authors discuss these challenges in detail and propose future research directions that have the potential to overcome these obstacles and further improve the performance of deep learning for insider threat detection. Overall, this survey highlights the importance of addressing insider threats in cyberspace and emphasizes how advanced deep learning techniques can contribute to more effective detection methods. By exploring new research directions and overcoming existing limitations, there is an opportunity to enhance the capabilities of deep learning models in identifying insider threats.