[Paper Review] Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities

Shuhan Yuan, Xintao Wu|arXiv (Cornell University)|May 25, 2020

Network Security and Intrusion Detection82 references19 citations

TL;DR

This paper reviews deep learning applications in insider threat detection, highlighting how models like RNNs, GNNs, and CNNs improve detection over traditional machine learning by learning complex behavioral patterns end-to-end. Despite gains, challenges like data scarcity and adaptive attacks limit performance, prompting calls for explainable AI, reinforcement learning, and better evaluation metrics such as cumulative recall (CR-k).

ABSTRACT

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.

Motivation & Objective

• To survey recent advances in deep learning for insider threat detection using real-world datasets and deep architectures.
• To identify key challenges in applying deep learning to insider threat detection, including data sparsity, lack of labeled threats, and adaptive attacks.
• To propose future research directions such as explainable AI, deep reinforcement learning, and improved evaluation metrics.
• To address the gap in comprehensive reviews focusing specifically on deep learning for insider threat detection.
• To advocate for better testbeds and metrics tailored to the high-stakes, low-frequency nature of insider threats.

Proposed method

• Surveying recent literature on deep learning models—specifically RNNs, CNNs, GNNs, and autoencoders—applied to insider threat detection using audit data.
• Analyzing the CERT dataset as a standard benchmark for insider threat research, noting its synthetic and simplified nature.
• Proposing deep reinforcement learning as a framework to learn optimal detection policies through interaction with environments, with potential use of inverse RL to infer reward functions.
• Integrating explainable AI techniques such as counterfactual explanations to improve model interpretability and fine-grained detection of malicious sequences.
• Evaluating models using CR-k, a recall-based metric that aggregates performance across multiple detection budgets to reflect real-world operational constraints.
• Combining meta-learning and imitation learning with deep reinforcement learning to mitigate data scarcity in low-shot insider threat scenarios.

Experimental results

Research questions

• RQ1 How do deep learning models compare to traditional machine learning in detecting insider threats across different data modalities?
• RQ2 What are the key challenges in applying deep learning to insider threat detection, particularly regarding data scarcity and adaptive attacker behavior?
• RQ3 Can deep reinforcement learning be effectively applied to insider threat detection, and what are the obstacles in designing appropriate reward functions?
• RQ4 How can explainable AI techniques enhance the trustworthiness and precision of deep learning models in high-stakes insider threat detection?
• RQ5 What evaluation metrics are most suitable for insider threat detection, and how can new metrics like CR-k better reflect operational performance?

Key findings

• Deep learning models such as RNNs and GNNs outperform traditional machine learning by automatically learning hierarchical representations from raw or minimally processed user behavior data.
• Despite performance gains, deep learning models for insider threat detection suffer from limited labeled data, especially for rare malicious events, which hinders training and generalization.
• Adaptive attacks—where insiders modify behavior to evade detection—pose a significant challenge, as current models often fail to generalize under evolving threat patterns.
• The cumulative recall (CR-k) metric is proposed as a more practical evaluation measure than standard F1 or AUC, as it reflects real-world detection budgets and prioritizes recall over precision.
• Explainable AI, particularly counterfactual explanations, shows promise in identifying specific behavioral patterns that trigger malicious predictions, enabling fine-grained threat analysis.
• Future work should focus on integrating meta-learning and imitation learning with deep reinforcement learning to reduce data dependency and improve robustness in low-data regimes.