Tony Thomas - Machine Learning Approaches in Cyber Security Analytics

With an overwhelming amount of data being generated and transferred over various networks, cybersecurity experts are having a hard time in monitoring everything that gets exchanged and identifying potential cyberthreats and attacks. As cyberattacks are becoming more frequent and sophisticated, there is a requirement for machines to predict, detect, and identify cyberattacks at a faster rate. Machine learning offers various tools and techniques to automate and quickly predict, detect, and identify cyberattacks. This book introduces various machine learning methods for cybersecurity analytics.

The main emphasis will be on the discussion of machine learning algorithms which have potential applications in cybersecurity analytics. There will be discussions on how cybersecurity analytics complements machine learning research. The potential applications include malware detection, biometrics, anomaly detection, cyberattack prediction, and so on.

The proposed book is a research monograph on cybersecurity analytics using various machine intelligence approaches. Most of the contents of the book are out of the original research by the authors. The cybersecurity and machine learning researchers, graduate students, and developers in cybersecurity will be benefited from this book. The prerequisites needed to understand the book are undergraduate-level knowledge mathematics, statistics, and computer science.

Nowadays, machine learning techniques have been applied in many areas such as voice recognition, fraud detection, email spam filtering, text processing, search recommendations, and video analysis. Machine learning techniques offer greater data analytics, reduction in the cost of computation, etc. Progress in the varied applications of machine learning techniques has also led to the advancement of the state of the art of machine learning research. In a broad sense, machine learning refers to a series of techniques where a machine is trained to solve a problem. Once a machine is trained, it can give solutions for several instances of the same problem.

Cybersecurity is a fast-growing field demanding a great deal of attention because of remarkable progresses in IoT networks, cloud and Web technologies, online banking, mobile environment, smart grid, etc. So with all of the recent technologies embracing machine learning approaches, one may ask what exactly is machine learning and how it is applied in cybersecurity analytics.

As cyberattacks are becoming more frequent and sophisticated, there is a requirement for machines to predict, detect, and identify cyberattacks at a faster rate. Machine learning offers various tools and techniques to automate and quickly predict, detect, and identify cyberattacks. Diverse machine learning methods have been successfully deployed to address several wide-ranging problems in cybersecurity. This book discusses and highlights different applications of machine learning in cybersecurity. This research monograph presents the latest research in cybersecurity analytics using machine intelligence methods. A major part of the contents of the book are out of the research work carried out by the authors. This book will be useful for cybersecurity and machine learning researchers, graduate students and security product developers.