Advanced Machine Learning for Cyber-Attack Detection in IoT Networks

Grab attention immediately with cutting-edge solutions: Advanced Machine Learning for Cyber-Attack Detection in IoT Networks by Dinh Thai Hoang, Nguyen Quang Hieu, Diep N. Nguyen, and Ekram Hossain offers a rigorous, practical guide to defending connected systems at scale.

This authoritative volume distills state-of-the-art machine learning techniques—from supervised and unsupervised methods to deep learning and federated approaches—into actionable strategies for identifying and mitigating cyber-attacks across IoT networks. Rich with clear explanations, algorithmic workflows, and real-world case discussions, the book bridges academic rigor and industry practice for readers who need results now.

Engineered for professionals and researchers in cybersecurity, network engineering, and IoT development, the text demonstrates how to design anomaly detection pipelines, optimize models for edge and resource-constrained devices, and integrate threat intelligence into automated defenses. Coverage includes intrusion detection, adversarial robustness, data-efficient learning, and deployment considerations for smart cities, industrial IoT, and consumer ecosystems—making it relevant to teams across North America, Europe, Asia-Pacific, and beyond.

Whether you’re a graduate student building a research agenda, a security architect hardening an enterprise fleet, or a developer deploying resilient IoT solutions, this book equips you with practical frameworks, reproducible methodologies, and insights from leading experts.

For a comprehensive, future-ready approach to IoT cybersecurity informed by the latest machine learning advances, choose Advanced Machine Learning for Cyber-Attack Detection in IoT Networks. Order your copy today and start transforming how your organization detects and responds to threats in connected environments.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.