Using Machine Learning to Enhance Cybersecurity Threat Detection
Nilesh Parihar, Priyanka Fernandes, Swati Tyagi, Anuj Tyagi, Mohit Tiwari, Ahmad Y. A. Bani Ahmad · 2025
In cybersecurity, machine learning is becoming more and more significant. Making threat detection more actionable, scalable, and efficient than using conventional methods that call for human intervention is the main goal of using machine learning in cybersecurity. Cybersecurity machine learning issues require efficient, systematic, and theoretical solutions. The subtleties of applying machine learning algorithms to lower cybersecurity threats are examined in this study. The rapid increase of data in cyberspace and the level of precision of attacks necessitate a proactive approach to security. The adversarial nature of online attacks, data quality, and model robustness are some of the subjects covered in this paper. In the context of cybersecurity, ethical issues are also covered, as is the requirement for accountable and transparent AI systems. Through a careful examination of current research and practical implementations, this research demonstrates the vast potential of machine learning (ML) in bolstering cybersecurity defenses. It shows the importance of a well-coordinated approach that combines human expertise with machine learning capabilities. As the environment of cyber threats evolves, organisations seeking to safeguard their data and digital assets must leverage ML's capabilities.