This book explores machine learning (ML) defenses against the many cyberattacks that make our
workplaces schools private residences and critical infrastructures vulnerable as a
consequence of the dramatic increase in botnets data ransom system and network denials of
service sabotage and data theft attacks. The use of ML techniques for security tasks has been
steadily increasing in research and also in practice over the last 10 years. Covering efforts
to devise more effective defenses the book explores security solutions that leverage machine
learning (ML) techniques that have recently grown in feasibility thanks to significant advances
in ML combined with big data collection and analysis capabilities. Since the use of ML entails
understanding which techniques can be best used for specific tasks to ensure comprehensive
security the book provides an overview of the current state of the art of ML techniques for
security and a detailed taxonomy of security tasks and corresponding ML techniques that can be
used for each task. It also covers challenges for the use of ML for security tasks and outlines
research directions. While many recent papers have proposed approaches for specific tasks such
as software security analysis and anomaly detection these approaches differ in many aspects
such as with respect to the types of features in the model and the dataset used for training
the models. In a way that no other available work does this book provides readers with a
comprehensive view of the complex area of ML for security explains its challenges and
highlights areas for future research. This book is relevant to graduate students in computer
science and engineering as well as information systems studies and will also be useful to
researchers and practitioners who work in the area of ML techniques for security tasks.
