Cybersecurity and privacy are critical to modern network systems. As various malicious threats have been launched that target critical online services--such as e-commerce, e-health, social networks, and other major cyber applications--it has become more critical to protect important information from being accessed. Data-driven network intelligence is a crucial development in protecting the security of modern network systems and ensuring information privacy.

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 threats
Latest technologies in data-driven privacy preservation, including differential privacy, federated learning, and homomorphic encryption
Key areas in adversarial machine learning, from both offense and defense perspectives
Descriptions of network anomalies and cyber threats
Background information on data-driven network intelligence for cybersecurity
Robust and secure edge intelligence for network anomaly detection against cyber intrusions
Detailed 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.

Auteur

Shengjie Xu, PhD, is an IEEE member and is an Assistant Professor in the Management Information Systems Department at San Diego State University, USA.

Yi Qian, PhD, is an IEEE Fellow and is a Professor in the Department of Electrical and Computer Engineering at the University of Nebraska-Lincoln, USA.

Rose Qingyang Hu, PhD, is an IEEE Fellow. She is also a Professor with the Electrical and Computer Engineering Department and the Associate Dean for Research of the College of Engineering, Utah State University, USA.