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172065880
Adversarial AI Attacks, Mitigations, and Defense Strategies: A cybersecurity professional's guide to AI attacks, threat modeling, and securing AI
Item #:
172065880
TZS 196238
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- “The book not only explains how adversarial attacks work but also shows you how to build your own test environment and run attacks to see how they can corrupt ML models. It's a comprehensive guide that walks you through the technical details and then flips to show you how to defend against these very same attacks.”– Elaine Doyle, VP and Cybersecurity Architect, SalesforceFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesUnderstand the unique security challenges presented by predictive and generative AIExplore common adversarial attack strategies as well as emerging threats such as prompt injectionMitigate the risks of attack on your AI system with threat modeling and secure-by-design methodsBook DescriptionAdversarial attacks trick AI systems with malicious data, creating new security risks by exploiting how AI learns. This challenges cybersecurity as it forces us to defend against a whole new kind of threat. This book demystifies adversarial attacks and equips you with the skills to secure AI technologies. Learn how to defend AI and LLM systems against manipulation and intrusion through adversarial attacks such as poisoning, trojan horses, and model extraction, leveraging DevSecOps, MLOps, and other methods to secure systems.This is a comprehensive guide to AI security, combining structured frameworks with practical examples to help you identify and counter adversarial attacks. Part 1 introduces the foundations of AI and adversarial attacks. Parts 2, 3, and 4 cover key attack types, showing how each is performed and how to defend against them. Part 5 presents secure-by-design AI strategies, including threat modeling, MLSecOps, and guidance aligned with OWASP and NIST. The book concludes with a blueprint for maturing enterprise AI security based on NIST pillars, addressing ethics and safety under Trustworthy AI.By the end of this book, you’ll be able to develop, deploy, and secure AI systems against the threat of adversarial attacks effectively.*Email sign-up and proof of purchase requiredWhat you will learnSet up a playground to explore how adversarial attacks workDiscover how AI models can be poisoned and what you can do to prevent thisLearn about the use of trojan horses to tamper with and reprogram modelsUnderstand supply chain risksExamine how your models or data can be stolen in privacy attacksSee how GANs are weaponized for Deepfake creation and cyberattacksExplore emerging LLM-specific attacks, such as prompt injectionLeverage DevSecOps, MLOps and MLSecOps to secure your AI systemWho this book is forThis book tackles AI security from both angles - offense and defence. AI developers and engineers will learn how to create secure systems, while cybersecurity professionals, such as security architects, analysts, engineers, ethical hackers, penetration testers, and incident responders will discover methods to combat threats to AI and mitigate the risks posed by attackers. The book also provides a secure-by-design approach for leaders to build AI with security in mind. To get the most out of this book, you’ll need a basic understanding of security, ML concepts, and Python.Table of ContentsGetting Started with AIBuilding Our Adversarial PlaygroundSecurity and Adversarial AIPoisoning AttacksModel Tampering with Trojan Horses and Model ReprogrammingSupply Chain Attacks and Adversarial AIEvasion Attacks against Deployed AIPrivacy Attacks – Stealing Models(N.B. Please use the Read Sample option to see further chapters)
| Publisher | Packt Publishing |
| Publication date | 26 July 2024 |
| Language | English |
| Print length | 586 pages |
| ISBN-10 | 1835087981 |
| ISBN-13 | 978-1835087985 |
| Item weight | 1.02 kg |
| Dimensions | 19.05 x 3.45 x 23.5 cm |
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AI & Machine Learning Editorial Review
Adversarial AI Attacks, Mitigations, And Defense Strategies: A Cybersecurity Professional's Guide To AI Attacks, Threat Modeling, And Securing AI With MLSecOps is an exceptional resource for understanding adversarial AI and securing AI systems, covering everything from foundational concepts to complex hands-on implementations. With a print length of 586 pages, this guide organizes critical topics into digestible segments, offering practical demonstrations, code examples, and thorough explorations of adversarial attacks and mitigation strategies. Readers have praised its comprehensive approach, which includes building tools like mobile apps and dockerized applications to concretely understand AI threats. The book’s practical examples, rooted in respected frameworks such as NIST and MITRE, enhance its value as a must-read for cybersecurity professionals eager to elevate their skills.
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Pros
- Comprehensive overview of AI security threats and defenses
- Hands-on examples enhance practical understanding
- Detailed coverage of threat modeling frameworks
- Accessible for beginners with no prior AI knowledge
- Practical applications like mobile app development included
Cons
- Can feel dense at times due to detail richness
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TZS 196238
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Features & Benefits
- Gain foundational hands-on understanding of AI and ML techniques.
- Learn to detect and defend against adversarial AI attacks.
- Explore both predictive and generative AI security challenges.
- Implement secure-by-design methodology with threat modeling and MLSecOps.
- Access practical examples leveraging OWASP, MITRE, and NIST standards.
- A comprehensive resource for cybersecurity professionals facing evolving AI threats.
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