Machine Learning and Cybersecurity—Trends and Future Challenges, 2nd Edition

Dear Colleagues,

This Special Issue of Electronics is dedicated to examining the symbiotic relationship between machine learning and cybersecurity, with a specific focus on its application, progress, and challenges. Encompassing a wide array of topics, including anomaly detection, behavior analysis, adversarial machine learning, and transparent model development, the scope is set to provide a comprehensive overview of current trends and future prospects at this dynamic intersection. The primary purpose of this Special Issue is to serve as a valuable resource for researchers and practitioners alike, offering deep insights and practical knowledge.

In terms of its contribution to the existing literature, this Special Issue will stand as a significant supplement. By delving into the integration of advanced ML algorithms into security systems, it will expand the evolving landscape of cyber threat detection and response. Furthermore, its exploration of adversarial machine learning will shed light on the critical need for the creation of models that can withstand sophisticated attacks. This Special Issue will also address a pressing concern in the field—transparency and interpretability in ML models—which are pivotal for ensuring ethical and regulatory compliance. By providing an encompassing overview of these critical facets, this Special Issue enriches the existing body of knowledge and offers a crucial reference for those engaged in research and practice within the domain of machine learning and cybersecurity. Researchers, practitioners, and policymakers alike will find this Special Issue to be a valuable compendium of knowledge in an era where safeguarding digital spaces is of paramount importance.

Dr. Wajeb Gharibi
Guest Editor

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