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