What this book covers
Chapter 1, Defining Machine Learning Security, explains what machine learning is all about, how it’s affected by security issues, and what impact security can have on the use of your applications from an overview perspective. This chapter also contains guidelines on how to configure your system for use with the source code examples.
Chapter 2, Mitigating Risk at Training by Validating and Maintaining Datasets, explores how ensuring that the data you’re using is actually the data that you think you’re using is essential because your model can be skewed by various forms of corruption and data manipulation.
Chapter 3, Mitigating Inference Risk by Avoiding Adversarial Machine Learning Attacks, gives an overview of the various methods to interfere directly with model development through techniques such as evasion attacks and model poisoning.
Chapter 4, Considering the Threat Environment, considers how hackers target machine learning models and their goals in doing so from an overview perspective. You will discover some basic coded techniques for avoiding many machine learning attacks through standard methodologies.
Chapter 5, Keeping Your Network Clean, gives detailed information on how network attacks work and what you can do to detect them in various ways, including machine learning techniques as your defense. In addition, you will discover how you can use predictive techniques to determine where a hacker is likely to strike next.
Chapter 6, Detecting and Analyzing Anomalies, provides the details on determining whether outliers in your data are anomalies that need mitigation or novelties that require observation as part of a new trend. You will see how to perform anomaly detection using machine learning techniques.
Chapter 7, Dealing with Malware, covers the various kind of malware and what to look for in your own environment. This chapter shows how to take an executable apart so that you can see how it’s put together and then use what you learn to generate machine learning features for use in detection algorithms.
Chapter 8, Locating Potential Fraud, explores the sources of fraud today (and it’s not just hackers), what you can do to detect the potential fraud, and how you can ensure that the model you build will actually detect the fraud with some level of precision. The techniques in this chapter for showing how to discern model goodness also apply to other kinds of machine learning models.
Chapter 9, Defending Against Hackers, contemplates the psychology of hackers by viewing hacker goals and motivations. You will obtain an understanding of why simply building the security wall higher and higher doesn’t work, and what you can do, in addition to building new security protections for your system.
Chapter 10, Considering the Ramifications of Deepfakes, looks at the good and the bad of deepfake technology. You will get an overview of the ramifications of deepfake technology for research, business, and personal use today. This chapter also demonstrates one technique for creating a deepfake model in detail.
Chapter 11, Leveraging Machine Learning for Hacking, explains how hackers view machine learning and how they’re apt to build their own models to use against your organization. We will consider the smart bot threat in detail.
Chapter 12, Embracing and Incorporating Ethical Behavior, explains how behaving ethically not only ensures that you meet both privacy and security requirements that may be specified by law but also has an implication with regard to security, in that properly sanitized datasets have natural security prevention features as well. In addition, you will discover how using properly vetted datasets saves you time, money, and effort in building models that actually perform better.