Book cover of Machine Learning & Security

Learning to Protect Systems
with Data & Algorithms

Dive into the science to separate the facts from the buzz

Machine learning is eating the world. As security practitioners, understanding what data science can do for you is rapidly gaining importance. Gaining a literacy in data science is the only way forward.

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.

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.

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Covered Topics

Fighting Spam

Learn how machine learning has contributed to the success of modern spam filters

Machine Learning Primer

Brush up on the properties of different classes of machine learning algorithms

Anomaly Detection

Quickly detect anomalies, including breaches, fraud, and impending system failure

Malware Classification

Conduct malware analysis by extracting useful information from computer binaries

Network Analysis

Uncover attackers within the network by finding patterns inside datasets

Anti-Abuse on the Consumer Web

Examine how attackers exploit consumer-facing websites and app functionality

Building Production Systems

Translate your machine learning algorithms from the lab to production

Adversarial Machine Learning

Understand the threat attackers pose to machine learning solutions

Testimonials

The future of security and safety online is going to be defined by the ability of defenders to deploy machine learning to find and stop malicious activity at Internet scale and speed. Chio and Freeman have written the definitive book on this topic, capturing the latest in academic thinking as well as hard-learned lessons deploying ML to keep people safe in the field.

Alex Stamos, Chief Security Officer at Facebook

An excellent practical guide for anyone looking to learn how machine learning techniques are used to secure computer systems, from detecting anomalies to protecting end users.

Dan Boneh, Professor of Computer Science at Stanford University & Co-director of the Stanford Computer Security Lab

If you've ever wondered what machine learning in security looked like, this book gives you an HD silhouette.

Nwokedi C. Idika, Ph.D., Software Engineer at Google, Security & Privacy Organization

Availability

The first edition of Machine Learning & Security (mlsec) was first published in February 2018. You can find it in print and ebook formats at your favorite bookstores.

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The Korean edition of Machine Learning & Security (머신 러닝을 활용한 컴퓨터 보안) was published in January 2019. You can find it in print and ebook formats at Aladin.

The French edition of Machine Learning & Security (Machine Learning et sécurité - Protéger les systèmes avec des données et des algorithmes) was published in February 2019. You can find it in print and ebook formats at Lisez and Amazon.

The Chinese edition of Machine Learning & Security (机器学习与安全：用数据和算法保护系统) was published in August 2019. You can find it in print and ebook formats at JD.com.

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About the Authors

Clarence Chio is a software engineer and entrepreneur who has given talks, workshops, and trainings on machine learning and security at DEF CON, BLACK HAT, and other security conferences/meetups across more than a dozen countries. He was previously a member of the security research team at Shape Security, a community speaker with Intel, and a security consultant for Oracle.

Clarence advises a handful of startups on security data science, and is the founder and organizer of the “Data Mining for Cyber Security” meetup group, the largest gathering of security data scientists in the San Francisco Bay Area. He holds a B.S. and M.S. in Computer Science from Stanford University, specializing in data mining and artificial intelligence.

Find him as @cchio on Twitter.

David Freeman is a research scientist/engineer at Facebook working on spam and abuse problems. He previously led anti-abuse engineering and data science teams at LinkedIn, where he built statistical models to detect fraud and abuse and worked with the larger machine learning community at LinkedIn to build scalable modeling and scoring infrastructure.

He is an author, presenter, and organizer at international conferences on machine learning and security, such as NDSS, WWW, and AISec, and has published more than twenty academic papers on mathematical and statistical aspects of computer security. He holds a Ph.D. in mathematics from UC Berkeley and did postdoctoral research in cryptography and security at CWI and Stanford University.

Learning to Protect Systems with Data & Algorithms