USRP-based Hardware Platform Verification of Artificial Noise-Driven Industrial IoT Security
HuanHuan Song, YanXu Zhu, YongHuang Liu
The security of industrial Internet of Things (IoT) is undeniable. However, the inherent broadcast nature of IoT wireless connectivity poses significant eavesdropping risks to industrial confidential information from unauthorized devices. Theoretical investigations into the security of artificial noise-aided multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) communication systems have achieved remarkable comprehensiveness. However, the uplink-downlink reciprocity of instantaneous channel state information (CSI), channel fading magnitude variations, and capture of signal energy thresholds profoundly impact the security performance of artificial noise. In this paper, we design and implement a configurable parameters three-node industrial wireless security testbed with configurable parameters, utilizing universal software radio peripheral (USRP) hardware platforms in the 3.5 GHz frequency band. The study deploys the null-space artificial noise algorithm on the implemented three-node USRP testbed with LabVIEW software for empirical validation. Empirical results further demonstrate that the proposed testbed not only features flexible reconfiguration capabilities, but also experimentally validates the enhanced security robustness of the null space artificial noise (AN) scheme.
International Conference on Electronic Information Technology (EIT)•Aug 22, 2025•0 citations