Paper Title
PROTECTING CYBERSPACE FROM INSIDER THREATS USING BEHAVIOURAL ANALYTICS AND MACHINE LEARNINGAbstract
Insider threats have become a significant cybersecurity challenge because legitimate users may misuse authorized access to organizational systems, applications, networks, and sensitive information. Unlike many external attacks, insider attacks can be difficult to identify because malicious activities may initially resemble normal user operations. This article examines the role of behavioural analytics and machine learning (ML) in identifying anomalous user activities and strengthening organizational cybersecurity. Behavioural analytics establishes a baseline of normal user behaviour by examining factors such as login patterns, access frequency, resource utilization, file activities, network communication, and data-transfer behaviour. Machine learning techniques can subsequently identify deviations from these patterns and generate risk indicators for potential insider threats. The study adopts a conceptual and secondary-data-based research methodology, reviewing cybersecurity literature, insider-threat frameworks, and established organizational security practices. The proposed framework integrates data collection, behavioural profiling, anomaly detection, risk scoring, and security response mechanisms. The discussion indicates that combining behavioural analytics with machine learning can improve the ability to identify previously unknown and evolving insider-threat patterns, while reducing dependence on predefined signatures. However, false positives, privacy concerns, explainability, biased datasets, and adversarial manipulation remain important implementation challenges. The study concludes that machine learning should complement rather than replace organizational policies, access controls, human investigation, and incident-response procedures. An effective insider-threat program therefore requires a balanced combination of technological, organizational, and human-centred controls. CERT/SEI\'s seventh edition of its insider-threat guide similarly emphasizes coordinated, proactive, enterprise-wide mitigation based on analysis of more than 3,000 insider-threat cases.
KEYWORDS : Insider Threats, Cybersecurity, Behavioural Analytics, Machine Learning, User Behaviour Analytics, Anomaly Detection, Cyber Threat Detection, Artificial Intelligence, Risk Assessment, Information Security.
