Название: Secure Communication for 5G and IoT NetworksАвтор: S. Velliangiri, M. Gunasekaran, P. Karthikeyan
Издательство: Springer
Год: 2022
Страниц: 248
Язык: английский
Формат: pdf (true), epub
Размер: 20.1 MB
This book highlights research on secure communication of 5G and the Internet of Things (IoT) Networks, along with related areas to ensure secure and Internet-compatible IoT systems. The authors not only discuss 5G and IoT security and privacy challenges, but also energy efficient approaches to improving the ecosystems through communication. The book addresses the secure communication and privacy of the 5G and IoT technologies, while also revealing the impact of IoT technologies on several scenarios in smart city design. Intended as a comprehensive introduction, the book offers in-depth analysis and provides scientists, engineers and professionals the latest techniques, frameworks and strategies used in 5G and IoT technologies.
Detection of points, contextual and collective anomalies, network intrusion, malware, ransomware, and intruders are the identified application areas of IoT security. The technologies explored for providing security in the 5G-IoT scenario are presented here:
Authentication and Access Control
Authentication is rendered to be the prime security mechanism in IoT applications. However, these mechanisms have been challenged by attacks like node impersonation, node bypassing, and node capture attacks. Access control involves identification, authentication, authorization, and accountability. Behrad et al. proposed a slice-specific authentication and access control (SSAAC) protocol for a 5G-IoT scenario using three network functions, namely Gateway virtual function provided by a third party, Gateway function repository, and radio resource control connection endpoint
Blockchain-Based Security
Adoption of blockchain technology in this 5G-IoT scenario, which usually includes a trusted intermediary, clearly enhances the robustness and trustworthiness of the data involved. A block basically contains the data on transactions, timestamp, cryptographic hash function, reference to the previous block, and smart contracts if required. Blockchain technology provides security based on the transparency of data and auditability, information distribution, robustness, and decentralization.
Machine Learning and Big Data–Based Security
The machine learning (ML), deep learning (DL), and big data technologies used to secure the IoT services have also been reviewed here. Edge computing platform ushered in the analytics revolution and also enhances the performance of complex applications like virtual and augmented reality, smart city applications, and so on. Establishment of a strong defensive framework and detection of active and passive attacks in IoT networks can be done based on ML techniques, which comprise supervised, unsupervised, and reinforcement learning methodologies. Supervised learning techniques include support vector machine (SVM), random forest (RF), Bayesian theorem, k-nearest neighbor (k-NN), decision tree (DT), neural network (NN), and ensemble learning. Techniques like principal component analysis (PCA) and k-means clustering comprise the unsupervised techniques.
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