Today, artificial intelligence (AI) and machine/deep learning (ML/DL) have become the hottest areas in
information technology. In our society, many intelligent devices rely on AI/ML/ DL algorithms/tools for
smart operation. Although AI/ML/DL algorithms/ tools have been used in many internet applications and
electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted
by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be
misled by changing the classification boundary, among many other attacks/threats. Such attacks can make
AI products dangerous to use.
While this discussion focuses on security issues in AI/ML/ DL- based systems (i.e., securing the intelligent
systems themselves), AI/ML/DL models/algorithms can actually also be used for cyber security (i.e., use
of AI to achieve security).
Since AI/ML/ DL security is a newly emergent field, many researchers and industry people cannot yet
obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture
of the challenges and solutions to related security issues in various applications. It explains how different
attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book
describes many sets of promising solutions to achieve AI security and privacy. The features of this book
have seven aspects:
1. This is the first book to explain various practical attacks and countermeasures to AI systems.
2. Both quantitative math models and practical security implementations are provided.
3. It covers both “securing the AI system itself” and “using AI to achieve security.”
4. It covers all the advanced AI attacks and threats with detailed attack models.
5. It provides multiple solution spaces to the security and privacy issues in AI tools.
6. The differences among ML and DL security/privacy issues are explained.
7. Many practical security applications are covered.