Machine Learning-Driven User Behavior Analytics for Insider Threat Detection

• Author(s): Eseoghene Daniel Erigha ; Ehimah Obuse ; Noah Ayanbode ; Emmanuel Cadet ; Edima David Etim
• Paper ID: 1710368
• Page: 535-555
• Published Date: 31-05-2019
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 2 Issue 11 May-2019

Abstract

Insider threats present a significant and often underestimated risk to organizational security, as they involve malicious or negligent activities originating from individuals with legitimate access to systems and sensitive information. Traditional rule-based and signature-driven detection methods are frequently inadequate against sophisticated insider behaviors that evolve over time and evade predefined thresholds. This paper explores a comprehensive framework for Machine Learning-Driven User Behavior Analytics (UBA) aimed at detecting insider threats through the continuous monitoring, profiling, and anomaly detection of user activities. The proposed approach leverages supervised, unsupervised, and deep learning algorithms to analyze high-dimensional datasets encompassing login patterns, file access histories, communication metadata, and application usage logs. Feature engineering is employed to extract temporal, contextual, and relational indicators of potentially malicious actions, while advanced models such as autoencoders, recurrent neural networks (RNNs), and graph-based anomaly detectors are applied to identify deviations from established behavioral baselines. The system incorporates adaptive learning capabilities to dynamically refine detection thresholds, thereby reducing false positives and enhancing detection accuracy in real time. Experimental evaluations are conducted using benchmark datasets and simulated enterprise environments to validate the robustness of the framework across various insider threat scenarios, including data exfiltration, privilege escalation, and policy violations. Results demonstrate that the proposed model achieves superior detection performance compared to conventional approaches, with improved precision, recall, and F1-scores, particularly in identifying low-and-slow attacks that unfold over extended periods. The study further addresses challenges related to data privacy, scalability, and interpretability by integrating privacy-preserving analytics, distributed processing architectures, and explainable AI techniques. Practical deployment considerations, including system integration, user acceptance, and compliance with regulatory standards, are also discussed. This research contributes to the field of cybersecurity by providing an intelligent, adaptive, and scalable insider threat detection model that aligns with modern enterprise needs, supporting proactive defense strategies against internal security breaches.

Keywords

Machine Learning, User Behavior Analytics, Insider Threat Detection, Anomaly Detection, Cybersecurity, Deep Learning, Behavioral Profiling, Autoencoders, Recurrent Neural Networks, Graph-Based Models, Explainable AI, Data Privacy, Adaptive Learning, Enterprise Security.

Citations

IRE Journals:
Eseoghene Daniel Erigha , Ehimah Obuse , Noah Ayanbode , Emmanuel Cadet , Edima David Etim "Machine Learning-Driven User Behavior Analytics for Insider Threat Detection" Iconic Research And Engineering Journals Volume 2 Issue 11 2019 Page 535-555

IEEE:
Eseoghene Daniel Erigha , Ehimah Obuse , Noah Ayanbode , Emmanuel Cadet , Edima David Etim "Machine Learning-Driven User Behavior Analytics for Insider Threat Detection" Iconic Research And Engineering Journals, 2(11)