Revolutionizing Cyber Security: Exploring the Synergy of Machine Learning and Logical Reasoning for Cyber Threats and Mitigation

The integration of machine learning (ML) and logical reasoning (LR) in cyber security is an emerging field that shows great potential for improving the efficiency and effectiveness of security systems. While ML can detect anomalies and patterns in large amounts of data, LR can provide a higher-level understanding of threats and enable better decision-making. This paper explores the future of ML and LR in cyber security and highlights how the integration of these two approaches can lead to more robust security systems. We discuss several use cases that demonstrate the effectiveness of the integrated approach, such as threat detection and response, vulnerability assessment, and security policy enforcement. Finally, we identify several research directions that will help advance the field, including the development of more explainable ML models and the integration of human-in-the-loop approaches.