Cybersecurity in Intelligent Networking Systems

Cybersecurity and privacy are critical to modern network systems, and data-driven network intelligence is a crucial development in protecting them. This book provides a comprehensive introduction to data-driven cybersecurity, privacy preservation, and adversarial machine learning, and describes a proposed novel system that helps provide robust and trustworthy safeguards with edge-enabled cyber infrastructure, edge-enabled AI engines, and threat intelligence.

Format: Hardback
Length: 144 pages
Publication date: 08 December 2022
Publisher: John Wiley and Sons Ltd

Cybersecurity and privacy are of paramount importance in modern network systems, given the increasing prevalence of malicious threats targeting critical online services like e-commerce, e-health, social networks, and other vital cyber applications. Protecting sensitive information from unauthorized access has become increasingly crucial, necessitating the development of robust cybersecurity measures. Data-driven network intelligence plays a pivotal role in safeguarding the security of these systems and ensuring information privacy.

Cybersecurity in Intelligent Networking Systems offers a comprehensive introduction to data-driven cybersecurity, privacy preservation, and adversarial machine learning. It provides a foundation for exploring technologies, applications, and issues related to data-driven cyber infrastructure. The book proposes a novel data-driven network intelligence system that incorporates edge-enabled cyber infrastructure, edge-enabled artificial intelligence (AI) engines, and threat intelligence to provide robust and trustworthy safeguards.

The book focuses on encryption-based security protocols and highlights the capabilities of a network intelligence system in detecting unauthorized access, malicious interactions, and the destruction of critical information and communication technology. It also delves into fundamental AI for cybersecurity, including artificial intelligence, machine learning, and security threats. Additionally, readers will gain insights into the latest technologies in data-driven privacy preservation, such as differential privacy, federated learning, and homomorphic encryption.

Adversarial machine learning is a key area covered in the book, encompassing both offensive and defensive strategies. The book provides a comprehensive overview of these techniques and their applications in the realm of cybersecurity. By leveraging data-driven network intelligence and advanced technologies, organizations can enhance their defenses against cyber threats, protect sensitive information, and maintain the trust of their stakeholders.

In conclusion, Cybersecurity in Intelligent Networking Systems is an essential reference work for professionals, researchers, and students interested in cybersecurity and privacy in modern network systems. It offers a comprehensive and up-to-date understanding of data-driven cybersecurity, privacy preservation, and adversarial machine learning, enabling individuals and organizations to stay ahead of the evolving threat landscape.

Weight: 346g
Dimension: 157 x 236 x 15 (mm)
ISBN-13: 9781119783916