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From the Publisher

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.

Publisher ‏ : ‎ O’Reilly Media; 1st edition (March 13, 2018)
Language ‏ : ‎ English
Paperback ‏ : ‎ 383 pages
ISBN-10 ‏ : ‎ 1491979909
ISBN-13 ‏ : ‎ 978-1491979907
Item Weight ‏ : ‎ 1.3 pounds
Dimensions ‏ : ‎ 7 x 0.8 x 9.1 inches

Machine Learning and Security: Protecting Systems with Data and Algorithms

In today’s digital age, the importance of cybersecurity cannot be overstated. With the ever-increasing number of cyber threats and attacks, it has become imperative for organizations to enhance their security measures to protect their systems and data.

One of the most powerful tools in the fight against cyber threats is machine learning. Machine learning algorithms can analyze vast amounts of data, identify patterns, and predict potential security breaches before they occur. By utilizing machine learning, organizations can proactively protect their systems and prevent attacks.

Machine learning can be used in a variety of ways to enhance security measures. For example, anomaly detection algorithms can detect unusual behavior on a network, such as unauthorized access or data exfiltration. Additionally, machine learning algorithms can be trained to recognize patterns in malware and phishing attacks, enabling organizations to quickly identify and mitigate these threats.

By harnessing the power of data and algorithms, organizations can significantly enhance their cybersecurity posture and protect their systems from a wide range of cyber threats. Machine learning is a powerful tool that can help organizations stay one step ahead of cybercriminals and safeguard their most valuable assets.
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