Teil der Reihe: Computer Science (R0)

Adversarial Example Detection and Mitigation Using Machine Learning

Name: Adversarial Example Detection and Mitigation Using Machine Learning
Brand: Ehsan Nowroozi
SKU: 9783031994470
Price: 160.49 EUR
Availability: InStock

€160,49

inkl. MwSt. • Kein physischer Versand

Sofort per Download lieferbar

Nach dem Kauf direkt als Download verfügbar.

Anzahl:

E-Book

eBook-Format:PDF

Inhaltsangabe

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.

Produktdetails