A lightweight D2D security protocol with request-forecasting for next-generation mobile networks

Daniel Gerbi Duguma, Jiyoon Kim, Sangmin Lee, Nam-Su Jho, Vishal Sharma, Ilsun You

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Abstract

5G-assisted device-to-device (D2D) communication plays an instrumental role in minimizing latency, maximizing resource utilization, improving speed, and boosting system capacity. However, the technology confronts several challenges to realize its enormous potential fully. Security and privacy concerns are at the top of the list that can jeopardize the regular operation of D2D communication by executing various assaults such as free-riding and impersonation. Although several researchers suggested different solutions to these concerns, most are too heavy for resource-constrained devices or are vulnerable to security risks. Consequently, we proposed a lightweight and provably secure D2D communication protocol comprising initialization, device discovery, and link setup phases. The protocol is light in terms of computational overhead and communication latency while verifiably secure through formal security analysis. The protocol relies on a new network function, called D2D Security Management Function (DSMF), located near the devices to facilitate secure communication and improve performance. Moreover, we used deep learning-based UE trust score forecasting to better handle and prioritize communication requests when the network is overloaded. The comparative analysis against state-of-the-art security schemes concerning computational and communication overheads shows that our protocol is a superior alternative for resource-constrained IoT devices wishing to perform D2D communication in a 5G network.
Original languageEnglish
Number of pages25
JournalConnection Science
Early online date30 Nov 2021
DOIs
Publication statusEarly online date - 30 Nov 2021

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