A Deep Learning-Based User Behavior Analytics Model for Proactive Cyber Threat Detection and Risk Management: A Review

Abstract

As cyber threats evolve, traditional rule-based systems struggle to detect complex and dynamic malicious behaviors, and the vulnerability of organizations to insider threats has drastically increased. Individuals entrusted with access or knowledge of the organization have become a significant concern. This review explores deep learning-based User Behavior Analytics and the integration of context-aware deep learning models for insider threat detection. Also, we investigate the use of hybrid models, including attention-based Long Short-Term Memory (LSTM) networks, which combine sequential modeling with attention mechanisms to enhance context-awareness and improve threat detection and proactive risk management. Furthermore, this paper highlights proactive risk management and dynamic interventions grounded on personalized risk profiling and user micro-segmentation. We further explore studies that give a deeper understanding of how these deep machine learning models align with ISO/IEC 27001:2022 standards, and how they can be integrated into existing frameworks to bolster proactive risk management efforts. By delivering insights into the future of AI-driven cybersecurity, this paper highlights the need to adapt to evolving threats and bolster the resilience of digital infrastructures through intelligent and adaptive security solutions.

The authors' abstract, as published at the source. F1000Research, 2026 · DOI ↗