图书简介
Today, Artificial Intelligence (AI) and Machine Learning/ 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 operations. Although AI/ML/DL algorithms and 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 and 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 and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security).Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals 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:This is the first book to explain various practical attacks and countermeasures to AI systemsBoth quantitative math models and practical security implementations are providedIt covers both "securing the AI system itself" and "using AI to achieve security"It covers all the advanced AI attacks and threats with detailed attack modelsIt provides multiple solution spaces to the security and privacy issues in AI toolsThe differences among ML and DL s
Preface
About the Editors
Contributors
Part I. Secure AI/ML Systems: Attack Models
1. Machine Learning Attack Models
Jing Lin, Long Dang, Mohamed Rahouti, and Kaiqi Xiong
2. Adversarial Machine Learning: A New Threat Paradigm for Next-generation Wireless Communications
Yalin E. Sagduyu, Yi Shi, Tugba Erpek, William Headley, Bryse Flowers, George Stantchev, Zhuo Lu, and Brian Jalaian
3. Threat of Adversarial Attacks to Deep Learning: A Survey
Linsheng He and Fei Hu
4. Attack Models for Collaborative Deep Learning
Jiamiao Zhao, Fei Hu, and Xiali Hei
5. Attacks on Deep Reinforcement Learning Systems: A Tutorial
Joseph Layton and Fei Hu
6. Trust and Security of Deep Reinforcement Learning
Yen- Hung Chen, Mu- Tien Huang, and Yuh- Jong Hu
7. IoT Threat Modeling using Bayesian Networks
Diego Heredia
Part II. Secure AI/ML Systems: Defenses
8. Survey of Machine Learning Defense Strategies
Joseph Layton, Fei Hu, and Xiali Hei
9. Defenses Against Deep Learning Attacks
Linsheng He and Fei Hu
10. Defensive Schemes for Cyber Security of Deep Reinforcement Learning
Jiamiao Zhao, Fei Hu, and Xiali Hei
11. Adversarial Attacks on Machine Learning Models in Cyber-Physical Systems
Mahbub Rahman and Fei Hu
12. Federated Learning and Blockchain: An Opportunity for Artificial Intelligence with Data Regulation
Darine Ameyed, Fehmi Jaafar, Riadh ben Chaabene, and Mohamed Cheriet
Part III. Using AI/ML Algorithms for Cyber Security
13. Using Machine Learning for Cyber Security: Overview
D. Roshni Thanka, G. J
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