Machine Learning in Cybersecurity : Applications, Challenges and Future Directions

Abstract

Machine learning (ML) is transforming cybersecurity by enabling advanced detection, prevention and response mechanisms. This paper provides a comprehensive review of ML's role in cybersecurity, examining both theoretical frameworks and practical implementations. It outlines the emerging threats targeting ML models, such as adversarial attacks, data poisoning and model inversion attacks and discusses state-of-the-art defense strategies, including adversarial training, robust architectures and differential privacy. Additionally, the paper explores various ML applications in cybersecurity from intrusion detection to malware classification, highlighting their impact on enhancing security measures. An anomaly inference algorithm is proposed for the early detection of cyber-intrusions at the substations. Cybersecurity has become a vital research area. The paper concludes with a discussion on the key research directions and best practices for creating secure and resilient ML systems in a data-driven world. This paper delves into how Machine Learning (ML) revolutionizes cybersecurity, empowering advanced detection, prevention, and response mechanisms. It offers a thorough exploration of ML's pivotal role in cybersecurity, encompassing theoretical frameworks and practical applications. It addresses emerging threats like adversarial attacks and data poisoning, alongside cutting-edge defense strategies such as adversarial training and robust architectures.