MACHINE LEARNING APPROACHES FOR PREDICTIVE CYBER THREAT INTELLIGENCE AND RISK MANAGEMENT

Abstract

Threats to cybersecurity are increasing in sophistication and frequency; hence, intelligence-based risk management requirements are also more demanding. Machine learning applications analyse the enormous data generated from the cyber environment and assist in anomaly detection and potential attack prediction in cyber threat intelligence. The paper discusses the use of supervised, unsupervised, and reinforcement learning approaches in cyber-risk management and their effectiveness in terms of threat detection and threat mitigation. This study aims to merge ML models with real-time data from cybersecurity threats; some considerable improvements in accuracy and recall are gained over classical models. In contrast, some challenges still exist regarding data quality, adversarial attacks, and model interpretability. Our results clearly show the potential for using ML threat intelligence for the improvement of proactive cybersecurity framework strategies. The study has highlighted key considerations and good practices for embedding ML into risk management approaches to support robust, adaptive defence mechanisms.