Machine learning has become a hot topic in computer security in the past couple of years as a technique that can counter advances in attacker competency. This book will provide security and software practitioners with a practical guide to approaching modern security with machine learning. If you have a surface-level understanding of machine learning, you’re ready to get started.
By explore a range of data mining techniques for solving security problems in security, such as spam, authentication, abuse, and malware, you’ll learn how to build how to build scalable systems to combat intruders—and perhaps put an end to the cat-and-mouse game between attackers and defenders.
- Learn why and when machine learning is useful in the context of security
- Get a broad range of examples across different use cases
- Scale security data mining systems for deployment on web-scale platforms
Table of Contents
Chapter 1. Why Machine Learning And Security?
Chapter 2. Classifying And Clustering
Chapter 3. Anomaly Detection
Chapter 4. Malware Analysis
Chapter 5. Network Traffic Analysis
Chapter 6. Protecting The Consumer Web
Chapter 7. Production Systems
Chapter 8. Adversarial Machine Learning
Appendix A. Supplemental Material For Chapter 2
Appendix B. Integrating Open Source Intelligence