Price: $49.99
(as of Nov 23,2024 04:37:00 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

In today’s rapidly evolving digital landscape, the rise of artificial intelligence (AI) has brought about a new wave of cyber threats and attacks. Adversarial AI attacks, also known as AI-based attacks, are becoming increasingly sophisticated and difficult to detect. As a cybersecurity professional, it is crucial to understand the potential risks posed by these attacks and to implement effective mitigation and defense strategies to protect your organization’s AI systems.

One of the key challenges in defending against adversarial AI attacks is the inherent vulnerability of AI algorithms to manipulation and exploitation. Attackers can exploit weaknesses in AI models to deceive or manipulate them, leading to potentially catastrophic consequences. Threat modeling is essential in identifying potential vulnerabilities and understanding how attackers might exploit them. By conducting thorough threat modeling exercises, cybersecurity professionals can identify potential attack vectors and develop targeted defense strategies to mitigate these risks.

Securing AI systems requires a comprehensive approach that combines traditional cybersecurity measures with specialized techniques tailored to the unique challenges posed by AI attacks. Machine learning security operations (MLSecOps) is a rapidly emerging field that focuses on securing AI systems by integrating security practices into the machine learning workflow. By implementing MLSecOps practices, organizations can proactively monitor and defend against adversarial AI attacks, enhance the robustness of their AI models, and ensure the integrity and confidentiality of their data.

In this guide, we will explore the latest trends in adversarial AI attacks, discuss effective mitigation and defense strategies, and provide practical tips for securing AI systems with MLSecOps. By staying informed and proactive in the face of evolving cyber threats, cybersecurity professionals can effectively safeguard their organizations’ AI systems and ensure the continued security and integrity of their data.
#Adversarial #Attacks #Mitigations #Defense #Strategies #cybersecurity #professionals #guide #attacks #threat #modeling #securing #MLSecOps