Machine Learning Security Principles

Thwart hackers by preventing, detecting, and misdirecting access before they can plant malware, obtain credentials, engage in fraud, modify data, poison models, corrupt users, eavesdrop, and otherwise ruin your day

Key FeaturesDiscover how hackers rely on misdirection and deep fakes to fool even the best security systemsRetain the usefulness of your data by detecting unwanted and invalid modificationsDevelop application code to meet the security requirements related to machine learningBook DescriptionBusinesses are leveraging the power of AI to make undertakings that used to be complicated and pricy much easier, faster, and cheaper. The first part of this book will explore these processes in more depth, which will help you in understanding the role security plays in machine learning.

As you progress to the second part, you’ll learn more about the environments where ML is commonly used and dive into the security threats that plague them using code, graphics, and real-world references.

The next part of the book will guide you through the process of detecting hacker behaviors in the modern computing environment, where fraud takes many forms in ML, from gaining sales through fake reviews to destroying an adversary’s reputation. Once you’ve understood hacker goals and detection techniques, you’ll learn about the ramifications of deep fakes, followed by mitigation strategies.

This book also takes you through best practices for embracing ethical data sourcing, which reduces the security risk associated with data. You’ll see how the simple act of removing personally identifiable information (PII) from a dataset lowers the risk of social engineering attacks.

By the end of this machine learning book, you’ll have an increased awareness of the various attacks and the techniques to secure your ML systems effectively.

What you will learnExplore methods to detect and prevent illegal access to your systemImplement detection techniques when access does occurEmploy machine learning techniques to determine motivationsMitigate hacker access once security is breachedPerform statistical measurement and behavior analysisRepair damage to your data and applicationsUse ethical data collection methods to reduce security risksWho this book is forWhether you’re a data scientist, researcher, or manager working with machine learning techniques in any aspect, this security book is a must-have. While most resources available on this topic are written in a language more suitable for experts, this guide presents security in an easy-to-understand way, employing a host of diagrams to explain concepts to visual learners. While familiarity with machine learning concepts is assumed, knowledge of Python and programming in general will be useful.

Table of ContentsDefining Machine Learning SecurityMitigating Risk at Training by Validating and Maintaining DatasetsMitigating Inference Risk by Avoiding Adversarial Machine Learning AttacksConsidering the Threat EnvironmentKeeping Your Network CleanDetecting and Analyzing AnomaliesDealing with MalwareLocating Potential FraudDefending against HackersConsidering the Ramifications of DeepfakesLeveraging Machine Learning against Hacking&

Size: A4(20*28cm)

Printing: 80 gm – color

Cover: Softcover