http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Adversarial example detection and mitigation using machine learning
This book offers a comprehensive exploration of the emerging threats and defense strategies in adversarial machine learning and AI security.It covers a broad range of topics, from federated learning attacks, adversarial defenses, biometric vulnerabilities, and security weaknesses in generative AI to quantum threats and ethical considerations.
Preface -- Part I Foundations of Adversarial Machine Learning -- Chapter 1 A Brief Survey of Emerging Threats to AI Security -- Chapter 2 Ethical Considerations and Regulatory Standards for Adversarial Defense -- Chapter 3 Vulnerability Detection: From Formal Verification to Large Language Models and Hybrid Approaches: A Comprehensive Overview -- Part II Attacks on AI Systems -- Chapter 4 Backdoor Attacks in Text Classification: Threats, Methods, and Emerging Challenges -- Chapter 5 Biometric Template-Based Reconstruction Attack in Machine Learning -- Chapter 6 Security Weaknesses of Code Generated by Generative AI -- Chapter 7 No More Paper Tigers: A Taxonomy of Realistic Adversarial Attacks on Machine Learning based Malware Detection -- Chapter 8 Adversarial Threats to Digital Twin Technology: A Taxonomy of Vulnerabilities and Attack Surfaces -- Chapter 9 Quantum Adversarial Artificial Intelligence in Secure Internet of Things Networks -- Part III Defense Techniques and Robustness Strategies -- Chapter 10 Detecting and Mitigating Adversarial Examples in Neural Networks: An Enhanced PGD Approach -- Chapter 11 The Role of Explainable AI (XAI) in Enhancing the Security of Machine Learning Systems Against Adversarial Attacks -- Chapter 12 Neurodevelopmental-Inspired Training Enhances Adversarial Robustness of a Primary Visual Cortex-Based Model -- Chapter 13 Evaluating and Defending Against Adversarial Attacks on LLM-Generated LSTM Models -- Chapter 14 Statistical Feature-Based Detection of Adversarial Noise and Patch Attacks in Image and Deepfake Analysis -- Chapter 15 Probabilistic Robustness in Deep Learning: A Concise yet Comprehensive Guide -- Part IV Federated Learning under Attack and Defense -- Chapter 16 Enhancing Federated Learning Security: Cluster-Based Strategies to Counter GAN-Poisoned Attacks -- Chapter 17 Defense Strategies in Federated Learning Against Adversarial Attacks -- Chapter 18 Dual Perspectives on GAN-Based Data Poisoning in Federated Learning: VagueGAN Attacks and Data Poisoning Detection -- Part V Applications and Case Studies -- Chapter 19 Cyber Risk Assessment in IT/OT Convergence using Machine Learning -- Chapter 20 Anomaly Detection Techniques in IoT Networks: Review and Comparative Analysis -- Chapter 21 Bridging the Gap from Research to Reality: Methods for Fortifying Mitigation Measures against Adversarial AI -- Index.
Ehsan Nowroozi, Rahim Taheri, Lucas Cordeiro, editors.
Includes bibliographical references and index.