Reprint

Security and Privacy in Networks and Multimedia

Edited by
August 2024
252 pages
  • ISBN978-3-7258-1861-7 (Hardback)
  • ISBN978-3-7258-1862-4 (PDF)
https://doi.org/10.3390/books978-3-7258-1862-4 (registering)

Print copies available soon

This book is a reprint of the Special Issue Security and Privacy in Networks and Multimedia that was published in

Computer Science & Mathematics
Engineering
Summary

The rapid advancement of technology necessitates the development of innovative solutions that maintain robust security and privacy across data networks and multimedia systems. This collection aims to advance the state of the art in network and multimedia security, offering innovative solutions to pressing challenges and contributing significantly to the security of our increasingly digital world. Key topics include resilient forecasting networks for smart cities, integrating collective intelligence predictors that mitigate cyberattack impacts, and comprehensive security measures for supply chains utilizing machine learning and blockchain technologies. This Special Issue also explores advanced detection methods, such as jamming detection in next-generation communication systems and format-preserving encryption for network layer privacy protection. Intrusion detection and AI-enhanced security feature prominently, with the methods presented including semi-supervised alert filtering and the Improved Sine Cosine Algorithm with deep learning for anomaly detection. Generative approaches, such as the SPE-ACGAN method, address class imbalance in network intrusion detection systems, while end-verifiable key frameworks enhance IoT security. This Special Issue also covers explainable security solutions, such as the detection of evasive malicious PDFs using ensemble learning, and advanced cryptographic techniques, including radio frequency fingerprinting for smart grid security and hierarchical key management for wireless sensor networks in medical environments.

Format
  • Hardback
License and Copyright
© 2024 by the authors; CC BY-NC-ND license
Keywords
bloom; cipher-block chaining (CBC); HEED protocol; heterogeneous WSN; key management; PRNG; rivest-cipher5 (RC5); WSNs; Internet of Things; RSA; security; support vector machine; wireless sensor networks; Generative Adversarial Network; Intrusion Detection System; imbalanced dataset; machine learning; unsupervised learning; malicious PDF detection; PDF malware; feature engineering; reverse mimicry attack; malicious content injection; shapely additive explanation; ensemble learning; explainable machine learning; network intrusion detection system; imbalanced network traffic; resampling method; cloud computing; security; feature selection; machine learning; artificial intelligence; intrusion detection; false positive; cyber security; alert fatigue; semi-supervised learning; prototype clustering; network privacy; format-preserving encryption; programmable networks; radio frequency fingerprinting; machine learning; deep learning; software-defined radio; Internet of Things; cybersecurity; smart city; smart grid; jamming detection; EVM; 5G; resource block; cybersecurity; supply chain systems; blockchain; validation; security monitoring; attack mitigation; electricity load forecasting; internet of things; machine learning; security; controller area network (CAN); bus-off attack; CAN attack detection; CAN attack response; n/a