O'Reilly Media (February 17, 2018) | ISBN: 1491979909 | 386 pages | PDF

                                                                   From the Preface                                                                   What’s In This Book?                                                                  

            We wrote this book to provide a framework for discussing the inevitable marriage of two ubiquitous concepts: machine learning and security. While there is some literature on the intersection of these subjects (and multiple conference workshops: CCS’s AISec, AAAI’s AICS, and NIPS’s Machine Deception), most of the existing work is academic or theoretical. In particular, we did not find a guide that provides concrete, worked examples with code that can educate security practitioners about data science and help machine learning practitioners think about modern security problems effectively.        

                    

            In examining a broad range of topics in the security space, we provide examples of how machine learning can be applied to augment or replace rule-based or heuristic solutions to problems like intrusion detection, malware classification, or network analysis. In addition to exploring the core machine learning algorithms and techniques, we focus on the challenges of building maintainable, reliable, and scalable data mining systems in the security space. Through worked examples and guided discussions, we show you how to think about data in an adversarial environment and how to identify the important signals that can get drowned out by noise.        

                                                                         Who Is This Book For?                                                                  

            If you are working in the security field and want to use machine learning to improve your systems, this book is for you. If you have worked with machine learning and now want to use it to solve security problems, this book is also for you.        

                    

            We assume you have some basic knowledge of statistics; most of the more complex math can be skipped upon your first reading without losing the concepts. We also assume familiarity with a programming language. Our examples are in Python and we provide references to the Python packages required to implement the concepts we discuss, but you can implement the same concepts using open source libraries in Java, Scala, C++, Ruby, and many other languages.