Machine Learning Security Principles : Keep data, networks, users, and applications safe from prying eyes

John Paul Mueller

ISBN 10: 1804618853 / ISBN 13: 9781804618851

Published by Packt Publishing, 2022

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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 Features:Discover how hackers rely on misdirection and deep fakes to fool even the best security systems Retain the usefulness of your data by detecting unwanted and invalid modifications Develop application code to meet the security requirements related to machine learning Book Description: Businesses 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 Learn:Explore methods to detect and prevent illegal access to your system Implement detection techniques when access does occur Employ machine learning techniques to determine motivations Mitigate hacker access once security is breached Perform statistical measurement and behavior analysis Repair damage to your data and applications Use ethical data collection methods to reduce security risks Who this book is for: Whether 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.

Seller Inventory # 9781804618851

Bibliographic Details

• Title: Machine Learning Security Principles : Keep data, networks, users, and applications safe from prying eyes
• Publisher: Packt Publishing
• Publication Date: 2022
• Language: English
• ISBN 10: 1804618853
• ISBN 13: 9781804618851
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 191H x 24D millimeters 235 millimeters width by 191 millimeters height by 24 millimeters depth
• Weight: 834 grams

About this title

Synopsis

Thwart hackers by preventing, detecting, and misdirecting attempted access before the hacker can obtain credentials, engage in fraud, dig in deep, modify data, corrupt users, and otherwise completely ruin your day.

Key Features

• Discover how hackers now rely on deep fakes to fool even the best security system.
• Retain the usefulness of your data by detecting unwanted and invalid modifications.
• Develop application code that addresses a number of security needs related to machine learning.

Book Description

Machine learning is the most important new technology for getting more out of data today. It can reveal patterns that aren't obvious, for example, but it requires data, lots of it. Data gathering isn't just about data. It affects users and requires the use of applications to clean, manipulate, and analyze the data. Obtaining data in an ethical manner is important because the very act behaving ethically reduces the security risk associated with data. However, hackers don't necessarily target users and their data. Perhaps they're interested in your organization's trade secrets or committing fraud. They might simply be interested in lurking in the background and committing mischief. So, just keeping your data secure as a means of protecting your machine learning investment isn't enough. You need to do more.

This book helps you get the big picture from a machine learning perspective using all the latest research available on methods that hackers use to break into your system. It's about the whole system, not just your application. You discover techniques that help you gather data ethically and keep it safe, while also preventing all sorts of illegal access method from even occurring. In fact, you use machine learning as a tool to keep hackers at bay and discover their true intent for your organization.

What you will learn

• Learn methods to prevent illegal access to your system
• Discover detection techniques when access does occur
• Employ machine learning techniques to determine motivations
• Mitigate hacker access using a variety of methods
• Repair damage to your data and applications
• Use ethical data collection methods to reduce security risks

Who This Book Is For

Data scientists and computer scientists who develop machine learning applications of any type including students will find this book helpful. Machine Learning and Security researchers and scientists that are involved in some type of theoretical activity that benefits from machine learning will benefit from this book. Having working knowledge of Python Programming is required to get the best from this book.

Table of Contents

1. Defining Machine Learning Security
2. Mitigating Risk at Training by Validating and Maintaining Datasets
3. Mitigating Inference Risk by Avoiding Adversarial Machine Learning Attacks
4. Considering the Threat Environment
5. Keeping Your Network Clean
6. Detecting and Analyzing Anomalies
7. Dealing with Malware
8. Locating Potential Fraud
9. Defending Against Hackers
10. Considering the Ramifications of Deepfakes
11. Leveraging Machine Learning Against Hacking
12. Embracing Ethical Behavior

About the Authors

John Paul Mueller is a seasoned author and technical editor. He has writing in his blood, having produced 121 books and more than 600 articles to date. The topics range from networking to artificial intelligence and from database management to heads-down programming. Some of his current books include discussions of data science, machine learning, and algorithms. He also writes about computer languages such as C++, C#, and Python. His technical editing skills have helped more than 70 authors refine the content of their manuscripts. John has provided technical editing services to a variety of magazines, performed various kinds of consulting, and he writes certification exams.

Rod Stephens has been a software developer, consultant, instructor, and author. He has written more than 30 books and 250 magazine articles covering such topics as three-dimensional graphics, algorithms, database design, software engineering, interview puzzles, C#, and Visual Basic. Rod's popular C# Helper and VB Helper websites receive millions of hits per year and contain thousands of tips, tricks, and example programs for C# and Visual Basic developers.