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Machine Learning Security Principles

E-BookEPUB0 - No protectionE-Book
450 Seiten
Englisch
Packt Publishingerschienen am30.12.2022
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.
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Verfügbare Formate
BuchKartoniert, Paperback
EUR53,10
E-BookEPUB0 - No protectionE-Book
EUR34,79

Produkt

KlappentextBusinesses 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.
Details
Weitere ISBN/GTIN9781804615409
ProduktartE-Book
EinbandartE-Book
FormatEPUB
Format Hinweis0 - No protection
FormatFormat mit automatischem Seitenumbruch (reflowable)
Erscheinungsjahr2022
Erscheinungsdatum30.12.2022
Seiten450 Seiten
SpracheEnglisch
Artikel-Nr.14287595
Rubriken
Genre9201