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Machine Learning and Security: Protecting Systems with Data and Algorithms 1st Edition

by Clarence Chio (Author), David Freeman (Author)
4.4 4.4 out of 5 stars 54 ratings
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Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis.

Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike.

  • Learn how machine learning has contributed to the success of modern spam filters
  • Quickly detect anomalies, including breaches, fraud, and impending system failure
  • Conduct malware analysis by extracting useful information from computer binaries
  • Uncover attackers within the network by finding patterns inside datasets
  • Examine how attackers exploit consumer-facing websites and app functionality
  • Translate your machine learning algorithms from the lab to production
  • Understand the threat attackers pose to machine learning solutions
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  1. ISBN-10
    1491979909
  2. ISBN-13
    978-1491979907
  3. Edition
    1st
  4. Publisher
    O'Reilly Media
  5. Publication date
    February 20, 2018
  6. Language
    English
  7. Dimensions
    7 x 0.8 x 9.19 inches
  8. Print length
    386 pages
  9. See all details
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From the Publisher

Machine Learning and Security: Protecting Systems with Data and Algorithms

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.

Editorial Reviews

About the Author

Clarence Chio is an engineer and entrepreneur who has given talks, workshops, and training courses on machine learning and security at DEF CON and other security/software engineering conferences and 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 BS and MS in computer science from Stanford University, specializing in data mining and artificial intelligence.

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 PhD in mathematics from UC Berkeley and did postdoctoral research in cryptography and security at CWI and Stanford University.

Product details

  • Publisher ‏ : ‎ O'Reilly Media; 1st edition (February 20, 2018)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 386 pages
  • ISBN-10 ‏ : ‎ 1491979909
  • ISBN-13 ‏ : ‎ 978-1491979907
  • Item Weight ‏ : ‎ 1.36 pounds
  • Dimensions ‏ : ‎ 7 x 0.8 x 9.19 inches
  • Customer Reviews:
    4.4 4.4 out of 5 stars 54 ratings

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Top reviews from the United States

  • Colby Mainard
    5.0 out of 5 stars Great starting point
    Reviewed in the United States on February 18, 2023
    Verified Purchase
    Really helpful for best models for certain subject matters. Some of the model types I hadn’t heard of before, so got value out of there at a minimum.
    Helpful
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  • Teru
    5.0 out of 5 stars A good and comprehensive book
    Reviewed in the United States on July 18, 2019
    Verified Purchase
    It’s a good book for both tech and non-tech persons. For tech persons it’s a good reference book for the approach we can consider to make use of machine learning to enhance security, and refer to the sample code for hands on. For non-tech persons, it’s still valuable about the high level how machine learning create value on security by just walkthrough the book.
    One person found this helpful
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  • Make War
    3.0 out of 5 stars A little misleading...
    Reviewed in the United States on March 2, 2019
    Verified Purchase
    Author writes an entire book on how to use data science and ML to secure your resources and sums it up by literally saying that there are no defenses against attacks that target ML. Maybe it’s just me but when I read a book and someone suggests all these novel ways to protect my network and then at the end says there’s really no defense it’s strikes me as odd.
    3 people found this helpful
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  • Dimitri K
    5.0 out of 5 stars Very decent coverage
    Reviewed in the United States on April 1, 2018
    Verified Purchase
    The book makes a decent attempt to cover this complicated area without going too deep into math. Of course it is hard to do, but overal its a good introduction.
    4 people found this helpful
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  • tawfiq
    5.0 out of 5 stars Great approach on bring cyber with security data
    Reviewed in the United States on September 29, 2019
    Verified Purchase
    Great book using ml on security data
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  • L. Wu
    5.0 out of 5 stars Very good book. I recommend it
    Reviewed in the United States on June 10, 2018
    Verified Purchase
    Reading it now. Very good book. I recommend it!
    One person found this helpful
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  • Ben Rothke
    5.0 out of 5 stars Excellent reference for the serious reader
    Reviewed in the United States on March 30, 2018
    Machine learning and security are all the rage. With the RSA Conference a little more than 2 weeks away, there will be plenty of firms on the expo floor touting their security solutions based on AI, deep learning, and machine learning.

    In Machine Learning and Security: Protecting Systems with Data and Algorithms, authors Clarence Chio and David Freeman have written a no-nonsense technical and practical guide showing how you can avoid that hype, and truly use machine learning to enhance information security.

    After a brief introduction to what machine learning is, the authors candidly write of the limitations of machine learning in security. They note that the notion that machine learning methods will always give good results across different use cases is categorically false. In real-world scenarios there are usually factors to optimize for other than precision recall or accuracy.

    For those that think that machine learning is the latest information security silver bullet, as good as this book is, it certainly won’t help them. But for those that know the limitations of machine learning, the authors suggest approaching it with equal parts enthusiasm and caution, remembering that not everything can instantly be made better with machine learning.

    Machine learning works alongside areas such as pattern recognition and computational statistics, and as such, the book is made for those with a strong background in programming, math, and statistics. Most of the programming samples are in Python.

    Current technologies like malware and virus classification, intrusion detection, malware classification, network protocol analysis and more are imperfect science. The promise of machine learning comes with many challenges. For those who are willing to invest in doing that, Machine Learning and Security is an indispensable reference.

    This is a serious book for those serious about integrating machine learning into the overall information security framework. The reader is expected to know the underlying mathematics and statistics, Python and other languages, and more importantly – how to integrate these into their security architecture. Titles like Machine Learning For Dummies may provide a good introduction to the topic, but it’s books like this that will take you there.
    15 people found this helpful
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  • Cyrus Olsen
    3.0 out of 5 stars Good intro
    Reviewed in the United States on December 17, 2018
    Verified Purchase
    Not as technically deep as I expected
    One person found this helpful
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Top reviews from other countries

  • Tibor Tot
    5.0 out of 5 stars excellent content
    Reviewed in Canada on June 24, 2020
    Verified Purchase
    An excellent book to read about ML within the security area.
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  • H
    5.0 out of 5 stars really good
    Reviewed in the United Kingdom on March 8, 2020
    Verified Purchase
    really good
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  • Jordan Bird
    5.0 out of 5 stars Very good -- recommended!
    Reviewed in the United Kingdom on May 18, 2018
    Verified Purchase
    Very good book so far (I'm halfway through it).
    Report