Machine Learning for Cybersecurity : Threat Detection and Mitigation

Abstract

"Machine Learning for Cybersecurity: Threat Detection and Mitigation" delves into the transformative role of machine learning in addressing contemporary cybersecurity challenges. This reprint provides an in-depth exploration of how advanced techniques such as deep learning, natural language processing, and explainable AI are revolutionizing intrusion detection, anomaly detection, and threat intelligence. With a focus on practical applications, it covers critical topics such as malware analysis, IoT and cloud security, blockchain security, adversarial attacks, and secure data sharing. Through this reprint, readers will gain insights into cutting-edge approaches for vulnerability assessments, authentication, and privacy preservation while exploring frameworks for implementing security-aware AI systems. This comprehensive resource is essential for researchers, practitioners, and policymakers striving to strengthen digital ecosystems. It offers both theoretical insights and actionable solutions, paving the way for innovative cybersecurity strategies to combat an ever-evolving threat landscape.