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 2021. This compelling book features a collection of selected and extended papers from the Second International Workshop on Deployable Machine Learning for Security Defense (MLHat 2021), held in August 2021. Spanning 157 pages, this first edition delves into crucial topics such as machine learning applications in security, as well as strategies for malware attack and defense. Ideal for researchers, practitioners, and anyone interested in the intersection of technology and cybersecurity, this book offers valuable knowledge and innovative approaches to enhance security defenses through machine learning. Don't miss the opportunity to expand your understanding of this vital field!