Deployable Machine Learning for Security Defense

Discover the cutting-edge insights in "Deployable Machine Learning for Security Defense" by Gang Wang, published by Springer Nature Switzerland AG in 2020. This first edition features 165 pages of expertly curated content from the First International Workshop on Deployable Machine Learning for Security Defense, MLHat 2020, held in August 2020. The book includes eight meticulously reviewed papers, selected from a pool of thirteen qualified submissions, showcasing the latest advancements and applications of machine learning in enhancing security measures. Whether you're a researcher, practitioner, or enthusiast in the field of cybersecurity, this book offers invaluable knowledge and practical approaches to deploying machine learning techniques for effective security defense. Don't miss the opportunity to deepen your understanding of this vital intersection of technology and security.