Exploiting Spatial Correlations: Group Key Generation for Secure IoT Device Networks

• Published in: IEEE transactions on consumer electronics 2024-12, p.1-1
• Main Authors: Omar, Mohammed, Badawy, Ahmed, Abualsaud, Khalid, Yaacoub, Elias, Guizani, Mohsen
• Format: Article
• Language: English
• Subjects: Antenna measurements; Antennas; Communication system security; Correlation; Data Privacy; Deep Learning; Differential Privacy; Internet of Things; IoT Security; Numerical models; Quantization (signal); Simulation; Wireless communication; Wireless sensor networks
• ISSN: 0098-3063; 1558-4127
• DOI: 10.1109/TCE.2024.3510612

Generating lightweight and efficient group secret keys (GSKs) by leveraging the inherent physical characteristics of the wireless communication channels for Internet of Things (IoT) devices has gained a lot of interest in recent times. GSKs play a crucial role in ensuring secure communication among IoT devices by encrypting broadcast control and alert messages. In this article, we propose a novel efficient approach to generating GSKs by exploiting the channel state information (CSI) of spatially correlated IoT nodes. We first implement a feedforward neural network (FNN) for identical channel feature generation for the nodes. We then employ a GSK scheme based on the generated channel features. We also propose a novel quantization technique named Adaptive Gaussian distribution-based quantization method with guard-band (AGDQG), that leverages discarded key bits from the GDQG quantization algorithm to improve various key metrics. Simulation results validate the performance of our proposed scheme, and the initial secret keys generated show excellent performance when assessed against key error rate, key generation rate, and entropy. The comparative analysis verifies the proposed quantization algorithm, and the results show that the GSK scheme demonstrates outstanding performance in the presence of an adversary.