Book Image

Machine Learning Security Principles

By : John Paul Mueller
Book Image

Machine Learning Security Principles

By: John Paul Mueller

Overview of this book

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.
Table of Contents (19 chapters)
1
Part 1 – Securing a Machine Learning System
5
Part 2 – Creating a Secure System Using ML
12
Part 3 – Protecting against ML-Driven Attacks
15
Part 4 – Performing ML Tasks in an Ethical Manner

Locating Potential Fraud

Fraud is deception that is perpetrated with either financial or personal gain in mind. Many people think about identity theft or credit card theft when thinking about fraud, but that’s only a very small part of the picture. A person claiming or being credited with another’s accomplishments or qualities is another form of fraud. The key word when thinking about fraud is deception, which takes many forms – for example, disinformation and hypocrisy. Consequently, it’s important to come up with a solid definition of what fraud is when working with ML applications and data, which is the goal of the first part of this chapter.

Determining fraud sources is essential with ML because fraud sources generate data – deceptive data. Yes, the data looks just fine, but when you study it in depth, it contains one or more of the five mistruths of data described in the Defining the human element section of Chapter 1, Defining Machine Learning...