Machine Learning for Cyber Security Threat Detection: A Comprehensive Model


  • Jennifer John , Sunil Sukumaran Department of Computer Engineering, University of Tulane


Machine Learning, Cyber Security, Threat Detection, Anomaly Detection, Behavioral Analysis, Cyber Threats, Dynamic Adaptation, Proactive Defense, Security Posture, Algorithm Integration



The ever-evolving landscape of cybersecurity threats demands innovative approaches for effective threat detection and mitigation. This paper presents a comprehensive model leveraging machine learning techniques to enhance cyber threat detection capabilities. The proposed model integrates advanced algorithms, anomaly detection methods, and behavioral analysis to provide a robust defense mechanism against a wide array of cyber threats. The model aims to adapt dynamically to emerging threats, offering a proactive approach to cybersecurity. Through extensive experimentation and evaluation, the effectiveness and efficiency of the proposed model are demonstrated, showcasing its potential to bolster the security posture of organizations in the face of evolving cyber threats.