Innovative Approaches for Insider Threat Detection: Using User Behavior Anomalies Hybrid Approach Detection

This Book introduces the rapidly evolving cybersecurity landscape, focusing on insider threats as a significant, multifaceted challenge for modern organizations. As digital ecosystems expand, so does the surface area for cyberattacks, particularly those initiated by insiders-employees, contractors, or trusted third parties-who exploit legitimate access for malicious, negligent, or accidental purposes. provides a detailed and critical analysis of the literature surrounding insider threat detection in cybersecurity, with particular emphasis on anomaly detection techniques, behavioral analytics, and the evolving application of artificial intelligence. This Book presents an in-depth analysis of the data collected to evaluate the performance of machine learning algorithms in the detection of insider threats based on behavioral, psychological, and technical profiling. Finally this encapsulates the culmination of this research into a structured conclusion, summarizing major findings, highlighting limitations, and offering a strategic roadmap for future work.

Pagina's: 216, Paperback, LAP LAMBERT Academic Publishing