Machine learning security principles : use various methods to keep data, networks, users, and applications safe from prying eyes / John Paul Mueller.

Format:

Book

Author/Creator:

Mueller, John Paul, author.

Language:

English

Subjects (All):

Machine learning.

Physical Description:

1 online resource (451 pages)

Edition:

1st ed.

Place of Publication:

Birmingham, England : Packt Publishing, Limited, [2022]

Summary:

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 dayKey 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.

Contents:

Cover

Title Page

Copyright and Credits

Foreword

Table of Contents

Preface

Part 1 – Securing a Machine Learning System

Chapter 1: Defining Machine Learning Security

Building a picture of ML

Why is ML important?

Identifying the ML security domain

Distinguishing between supervised and unsupervised

Using ML from development to production

Adding security to ML

Defining the human element

Compromising the integrity and availability of ML models

Describing the types of attacks against ML

Considering what ML security can achieve

Setting up for the book

What do you need to know?

Considering the programming setup

Summary

Chapter 2: Mitigating Risk at Training by Validating and Maintaining Datasets

Technical requirements

Defining dataset threats

Learning about the kinds of database threats

Considering dataset threat sources

Delving into data change

Delving into data corruption

Uncovering feature manipulation Generated by AI.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

Description based on print version record.

Includes index.

Other Format:

Print version: Mueller, John Paul Machine Learning Security Principles

ISBN:

9781804615409

1804615404

OCLC:

1356573671