ABSTRACT
The collection, storage, manipulation, analysis, and retention of massive amounts of data have resulted in serious security and privacy considerations. Data science techniques have been used extensively for cyber security applications over the past two decades. Data scientists aggregate, process, analyze, and visualize big data in order to derive useful insights. Machine learning focuses on learning from prior experience and making predictions. This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book is divided into four parts, each describing some aspect of the technology that is relevant to data science and cyber security. It provides a detailed overview of the data science techniques. The book discusses the use of cloud for scalable data science techniques for malware analysis. It discusses attacks to data science systems and describes our approach to adversarial machine learning in general and support vector machine learning in particular. The book describes adversarial relevance vector machine learning.
