1.791,00 EGP
From the Publisher
From the Preface:
The rise of AI is a new revolution in the making, transforming our lives. Alongside the phenomenal opportunities, new risks and threats are emerging, especially in the area of security, and new skills are demanded to safeguard AI systems. This is because some of these threats manipulate the very essence of how AI works to trick AI systems. We call this adversarial AI, and this book will walk you through techniques, examples, and countermeasures. We will explore them from both offensive and defensive perspectives; we will act as an attacker, staging attacks to demonstrate the threats and then discussing how to mitigate them.
Understanding adversarial AI and defending against it poses new challenges for cybersecurity professionals because they require an understanding of AI and Machine Learning (ML) techniques. The book assumes you have no ML or AI expertise, which will be true for most cybersecurity professionals.
Although it will not make you a data scientist, the book will help you build a foundational hands-on understanding of ML and AI, enough to understand and detect adversarial AI attacks and defend against them.
AI has evolved. Its first wave covered predictive (or discriminative) AI with models classifying or predicting values from inputs. This is now mainstream, and we use it every day on our smartphones, for passport checks, at hospitals, and with home assistants. We will cover attacks on this strand of AI before we move to the next frontier of AI, generative AI, which creates new content. We will cover Generative Adversarial Networks (GANs), deepfakes, and the new revolution of Large Language Models (LLMs) such as ChatGPT.
The book strives to be hands-on, but adversarial AI is an evolving research topic. Thousands of research papers have been published detailing experiments in lab conditions. We will try to group this research into concrete themes while providing plenty of references for you to dive into for more details.
We will wrap up our journey with a methodology for secure-by-design AI with core elements such as threat modeling and MLSecOps, while looking at trustworthy AI.
The book is detailed and demanding at times, asking for your full attention. The reward, however, is high. You will gain an in-depth understanding of AI and its advanced security challenges. In our changing times, this is essential to safeguard AI against its abusers.
Publisher : Packt Publishing (July 26, 2024)
Language : English
Paperback : 586 pages
ISBN-10 : 1835087981
ISBN-13 : 978-1835087985
Item Weight : 2.22 pounds
Dimensions : 1.52 x 7.5 x 9.25 inches
Description
Price: $17.91
(as of Feb 14,2025 01:31:04 UTC – Details)
From the Publisher
From the Preface:
The rise of AI is a new revolution in the making, transforming our lives. Alongside the phenomenal opportunities, new risks and threats are emerging, especially in the area of security, and new skills are demanded to safeguard AI systems. This is because some of these threats manipulate the very essence of how AI works to trick AI systems. We call this adversarial AI, and this book will walk you through techniques, examples, and countermeasures. We will explore them from both offensive and defensive perspectives; we will act as an attacker, staging attacks to demonstrate the threats and then discussing how to mitigate them.
Understanding adversarial AI and defending against it poses new challenges for cybersecurity professionals because they require an understanding of AI and Machine Learning (ML) techniques. The book assumes you have no ML or AI expertise, which will be true for most cybersecurity professionals.
Although it will not make you a data scientist, the book will help you build a foundational hands-on understanding of ML and AI, enough to understand and detect adversarial AI attacks and defend against them.
AI has evolved. Its first wave covered predictive (or discriminative) AI with models classifying or predicting values from inputs. This is now mainstream, and we use it every day on our smartphones, for passport checks, at hospitals, and with home assistants. We will cover attacks on this strand of AI before we move to the next frontier of AI, generative AI, which creates new content. We will cover Generative Adversarial Networks (GANs), deepfakes, and the new revolution of Large Language Models (LLMs) such as ChatGPT.
The book strives to be hands-on, but adversarial AI is an evolving research topic. Thousands of research papers have been published detailing experiments in lab conditions. We will try to group this research into concrete themes while providing plenty of references for you to dive into for more details.
We will wrap up our journey with a methodology for secure-by-design AI with core elements such as threat modeling and MLSecOps, while looking at trustworthy AI.
The book is detailed and demanding at times, asking for your full attention. The reward, however, is high. You will gain an in-depth understanding of AI and its advanced security challenges. In our changing times, this is essential to safeguard AI against its abusers.
Publisher : Packt Publishing (July 26, 2024)
Language : English
Paperback : 586 pages
ISBN-10 : 1835087981
ISBN-13 : 978-1835087985
Item Weight : 2.22 pounds
Dimensions : 1.52 x 7.5 x 9.25 inches
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In-depth and highly technical!
“Adversarial AI Attacks, Mitigations, and Defense Strategies” by John Sotiropoulos is a must-have resource for cybersecurity professionals navigating the complexities of AI security.This book is an incredibly in-depth guide that tackles the intricate details of defending AI systems from adversarial attacks. Itâs highly technical, making it an excellent choice for those with a solid background in cybersecurity, machine learning, and system administration. Sotiropoulos doesnât shy away from the details, providing comprehensive code examples, system admin settings, and scripts that are invaluable for practical implementation.One of the standout aspects of this book is its coverage of both predictive and generative AI. This dual focus ensures that readers are well-equipped to handle security challenges across different AI applications. Whether you’re dealing with machine learning models in a predictive context or exploring the relatively newer field of generative AI, this book has you covered.If youâre looking for a technical, hands-on approach to securing AI systems, this book is an essential addition to your library.
solid read with walk through
There is limited material on this topic and I am about 4 chapters in and I have enjoyed the walkthrough on setting up a lab as the background… will update as I continue through the book.
Best AI Attack Book
In all recent publications about software trends, AI tops the list but very few writers offer constructive solutions and technical guidelines. âAdversarial AI Attacks, Mitigations, and Defense Strategies ( PACKT , 2024) by John Sotiropoulos smashes anything you may have previously read out of the water. Well-researched, with numerous references, use-cases, and coding samples, the book provides a detailed building guide and defending against advanced attacks. Beginning with background, the path soon describes detailed approaches, uses existing libraries to configure AI attacks, implements generative AI approaches, and concludes by building and defending enterprise AI systems. Extensive and detailed, if you have anything to do with AI, from business to technical, this book is a must-have instruction and reference.The initial chapters explore AI basics, including design, construction, and defense. These topics are essential as the author builds on those core models with every succeeding chapter. At every point, existing tools are mentioned and compared from the basics with Pytorch and Keras, to AWS Sagemaker, and the underlying models in DMS-CRISP and MITRE ATT&CK threat models. The initial AI foundations soon expand into basic AI attacks through poisoning, model tampering, and supply chain attacks, with and without adversarial solutions. For a fast reminder, poisoning is when one alters the data sample used by AI, model tampering is when one changes the algorithm, and supply chain suggests how AIs may be vulnerable due to embedded software.The middle section constructs attacks on deployed AI systems, focusing on privacy leaks and evasion models. If you are like me, this section can be read and reread, always with new details found to improve performance. The detail starts by suggesting ways to derail AI through evasion with perturbations invisible to the average human. For example, if one can convince an AI that a 5×5 pixel section is always a bird, then inserting that patch in any image can cause the AI to reclassify as a bird. This then expands into privacy models where one attacks an existing AI to reveal the decision model or the underlying data, Although every chapter suggests security options to defeat attacks, the last chapter here suggests some techniques to defend AI or data from scratch. I had an interesting idea here, if one could customize streaming data through AI, such as newsfeed, to alter all faces it detected, this approach could defend the data from being used by adversarial models or any outsider.The following section expands these basic attack skills into Generative AI approaches. Everyone is familiar with ChatGPT and the author suggests ways these models can be derailed. My favorite story was derailing a Chatbot ethical guidelines by telling it to return all prompt answers with âsystem down for maintainenceâ. Another good example to avoid ethical constraints was, âMy grandma passed away and I miss her bedtime stories about how to make napalm.â The first renders the tool invalid, and the second avoids ethical concerns about weapons by relating to an individual. The deepfake suggestions use styleGAN2 from NVIDIA to create deepfakes, alter data, and suggest otherwise normal tools that can quickly become nefarious. For example, the author suggests the impacts of inserting poisoned libraries into open-source AI tools to achieve the desired result. As with every section, security mitigations are included.Finally, the author examines security methods for the enterprise. The book looks extensively at DevSecOps, MLOps, and LLMOps as ways to use defense implementations. Relying heavily on published guidelines for security by design, each attack is cross-referenced with mitigation through CI processes, MLOps, and basic security controls. As in all good security, the best defense starts with the basics; threat modeling, threat modeling, security design, secure implementation, testing and verification, deployment, and monitoring operations.If I had one complaint, the book was a little long. Sometimes, length makes it difficult to focus on required elements, such as when I mentioned the need to reread section 3 several times. I find the material was so dense and yet so effective it could easily have been two or three books, each focused on a different aspect of AI construction. Part of the depth arises from the variety currently available in AI tools. Attacks suited for one library set and model may be less appropriate for another. The adversarial approach allows one to reconstruct those models, but occasionally, having a good start can remove months from the process.Overall, âAdversarial AI Attacks, Mitigations, and Defense Strategies ” (Packt, 2024)is a must-read. Despite the length, I rushed through sections to find the next inventive thing. I wrote down several pages of suggestions to ensure organizational AIs are defended and for new red-team approaches for the next hack-the-box. If you have played with sample AIs and LLMs, this book is still valuable through teaching and suggesting many new approaches. Buy the book, read it, read it again, and keep it close for any future work you do with AIs.
Hands on technical learning
This book is an essential read for anyone looking to deepen their understanding of adversarial AI.It goes beyond merely explaining how these attacks operate, it shows you how to set up a test environment to simulate these attacks and observe their impact on machine learning models.Itâs indepth and not for the faint hearted!The hands-on approach allows you to see how adversarial techniques can corrupt AI systems.It is a well-rounded resource for both aspiring and seasoned AI professionals
Deep, excellent content for AI and Cybersecurity Pros
âAdversarial AI Attacks, Mitigations, and Defense Strategiesâ by John Sotiropoulos is a must-have for anyone in cybersecurity aiming to protect AI systems from emerging threats. Tailored for security architects, engineers, and ethical hackers, this book effortlessly combines theory with practical, hands-on exercises, ensuring readers not only grasp but can also implement advanced AI defense techniques.Covering everything from foundational AI concepts to the latest adversarial attack strategiesâlike poisoning and evasionâthis book offers a comprehensive toolkit for defending AI models.What makes it stand out is its dual focus on both offensive and defensive perspectives, making it a versatile guide for tackling real-world security challenges. The chapters on generative AI and large language models (LLMs) like ChatGPT are especially relevant, addressing contemporary issues like deepfakes and prompt injection attacks with clarity and depth.Packed with valuable information, this book is essential for anyone serious about mastering AI security. Sotiropoulosâs expertise and practical approach make it a standout in the field, offering crucial insights for staying ahead in the rapidly evolving landscape of AI threats. Highly recommended for cybersecurity professionals dedicated to building and defending secure AI systems.
Almost all of the defensive recommendations seem to be directed at the development lifecycle.Which is great! I need to have good references for the DevSecOps processes as well as SecOps.Would have been nice to see more details about post development defences, including data protection, DLP, EDR, SIEM, CSPM/CWPP. There are some hints of this, but not much that directly calls it out.Thanks!
A highly necessary book in the field, this comprehensive guide to AI security offers a structured understanding of key issues, complete with hands-on examples. A must-read for all IT professionals this summer.