{"product_id":"adversarial-example-detection-and-mitigation-using-machine-learning-ebook","title":"Adversarial Example Detection and Mitigation Using Machine Learning","description":"\u003cp\u003ePreface.- Part I \u003cspan lang=\"EN-GB\" style=\"font-size: 12.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-GB; mso-fareast-language: EN-GB; mso-bidi-language: AR-SA;\"\u003eFoundations 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.\u003c\/span\u003e\u003c\/p\u003e","brand":"Ehsan Nowroozi","offers":[{"title":"Default Title","offer_id":53628260286791,"sku":"9783031994470","price":160.49,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0920\/5455\/2903\/files\/adversarial-example-detection-and-mitigation-using-ebook.webp?v=1775150040","url":"https:\/\/www.cinebuch.de\/products\/adversarial-example-detection-and-mitigation-using-machine-learning-ebook","provider":"CineBuch","version":"1.0","type":"link"}