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Full Description
A major challenge for machine learning solutions is that their efficiency in real-world applications is constrained by the current lack of ability of the machine to explain its decisions and activities to human users. Biases based on race, gender, age or location have been a long-standing risk in training AI models. Furthermore, AI model performance can degrade because production data differs from training data.
Explainable AI (XAI) is the practice of interpreting how and why a machine learning algorithm estimates its predictions. It can also help machine learning practitioners and data scientists understand and interpret a model's behaviour. XAI supports end-users to trust a model's auditability and the productive use of AI. It also mitigates AI compliance, legal, security and reputational risks.
Among these applications, the security of IoT infrastructures is vitally essential for improving trust in broad-scale applications such as smart healthcare, smart manufacturing, smart agriculture and smart transportation.
This comprehensive co-authored book offers a complete study of explainable artificial intelligence (XAI) for securing the Internet of things (IoT). The authors present innovative XAI solutions for securing IoT infrastructures against security attacks and privacy threats and cover advanced research topics including responsible security intelligence.
Providing a systematic and thorough overview of the field, this book will be a valuable resource for ICT researchers, AI and data science engineers, security analysts, undergraduate and graduate students and professionals who wish to gain a fundamental understanding of intelligent security solutions.
Contents
Chapter 1: Explaining AI for safeguarding and securing Internet of Things (IoT) systems - an introduction
Chapter 2: Securing the Internet of Things: architectures and designs
Chapter 3: Convergence of Internet of Things and computing technologies
Chapter 4: Security vulnerabilities in Internet of Things: attack surfaces, threats, and defense
Chapter 5: Black-box machine learning for IoT security
Chapter 6: Explainable artificial intelligence for safeguarding IoT
Chapter 7: Explainability methods in explainable security intelligence: fine-grained taxonomy
Chapter 8: Intrinsically explainable security intelligence
Chapter 9: Model-agnostic methods for globally interpretable machine learning
Chapter 10: Model-agnostic methods for locally explainable AI to secure IoT system
Chapter 11: Explainability evaluation metrics for explainable security intelligence in the Internet of Things
Chapter 12: Adversarial attacks and defense in explainable security intelligence
Chapter 13: Federated learning meets explainable AI at the edge of things
Chapter 14: Explainable security intelligence for zero-trust IoT
Chapter 15: Explainable security intelligence in IoT applications
