The damage that cyber threats can wreak upon an organization can be incredibly costly. In this book, we use the most efficient and effective tools to solve the big problems that exist in the cybersecurity domain and provide cybersecurity professionals with the knowledge they need to use machine learning algorithms. This book aims to bridge the gap between cybersecurity and machine learning, focusing on building new and more effective solutions to replace traditional cybersecurity mechanisms and provide a collection of algorithms that empower systems with automation capabilities.
This book walks you through the major phases of the threat life cycle, detailing how you can implement smart solutions for your existing cybersecurity products and effectively build intelligent and future-proof solutions. We'll look at the theory in depth, but we'll also study practical applications of that theory, framed in the contexts of real-world security scenarios. Each chapter is focused on self-contained examples for solving real-world concerns using machine learning algorithms such as clustering, k-means, linear regression, and Naive Bayes.
We begin by looking at the basics of machine learning in cybersecurity using Python and its extensive library support. You will explore various machine learning domains, including time series analysis and ensemble modeling, to get your foundations right. You will build a system to identify malicious URLs, and build a program for detecting fraudulent emails and spam. After that, you will learn how to make effective use of the k-means algorithm to develop a solution to detect and alert you about any malicious activity in the network. Also, you'll learn how to implement digital biometrics and fingerprint authentication to validate whether the user is a legitimate user or not.
This book takes a solution-oriented approach to helping you solve existing cybersecurity issues.
