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
About This Audiobook
- 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
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 audiobook 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 audiobook 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 audiobook 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 audiobook, you'll have an increased awareness of the various attacks and the techniques to secure your ML systems effectively.
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.
Table of contents
- Opening Credits
- Foreword and Contributors
- Chapter 1: Defining Machine Learning Security
- Chapter 2: Mitigating Risk at Training by Validating and Maintaining Datasets
- Chapter 3: Mitigating Inference Risk by Avoiding Adversarial Machine Learning Attacks, Part 1
- Chapter 3: Mitigating Inference Risk by Avoiding Adversarial Machine Learning Attacks, Part 2
- Chapter 4: Considering the Threat Environment
- Chapter 5: Keeping Your Network Clean
- Chapter 6: Detecting and Analyzing Anomalies
- Chapter 7: Dealing with Malware
- Chapter 8: Locating Potential Fraud
- Chapter 9: Defending against Hackers
- Chapter 10: Considering the Ramifications of Deepfakes
- Chapter 11: Leveraging Machine Learning for Hacking
- Chapter 12: Embracing and Incorporating Ethical Behavior
- Closing Credits
- Title: Machine Learning Security Principles
- Release date: February 2023
- Publisher(s): Packt Publishing
- ISBN: 9781805124788
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