000			02579nam a22001937a 4500
005			20250915145311.0
020			_a9781032034058
082			_a006.31 _bAIM
245			_aAI, Machine Learning and Deep Learning : _bA Security Perspective/ _cEdited By Fei Hu, Xiali Hei
260			_aLondon: _bCRC Press, _c2023.
300			_a346 p. _b136 B/W Ill.
520			_aToday, 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.
650			_aAI _91389
650			_aMachine Learning _9470
650			_aDeep Learning _9203
700			_aHu, Fei (Ed.) _91390
700			_aHei, Xiali (Ed.) _91391
942			_2ddc _cBK
999			_c751357 _d751357