Cybersecurity in Intelligent Networking Systems provides a background introduction to data-driven cybersecurity, privacy preservation, and adversarial machine learning. It offers a comprehensive introduction to exploring technologies, applications, and issues in data-driven cyber infrastructure. It describes a proposed novel, data-driven network intelligence system that helps provide robust and trustworthy safeguards with edge-enabled cyber infrastructure, edge-enabled artificial intelligence (AI) engines, and threat intelligence. Focusing on encryption-based security protocol, this book also highlights the capability of a network intelligence system in helping target and identify unauthorized access, malicious interactions, and the destruction of critical information and communication technology.
Cybersecurity in Intelligent Networking Systems readers will also find:
Fundamentals in AI for cybersecurity, including artificial intelligence, machine learning, and security threatsLatest technologies in data-driven privacy preservation, including differential privacy, federated learning, and homomorphic encryptionKey areas in adversarial machine learning, from both offense and defense perspectivesDescriptions of network anomalies and cyber threatsBackground information on data-driven network intelligence for cybersecurityRobust and secure edge intelligence for network anomaly detection against cyber intrusionsDetailed descriptions of the design of privacy-preserving security protocols
Cybersecurity in Intelligent Networking Systems is an essential reference for all professional computer engineers and researchers in cybersecurity and artificial intelligence, as well as graduate students in these fields.

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