InaudibleKey2.0: Deep Learning-Empowered Mobile Device Pairing Protocol Based on Inaudible Acoustic Signals

The increasing proliferation of Internet-of-Things (IoT) devices in daily life has rendered secure Device-to-Device (D2D) communication increasingly crucial. Achieving secure D2D communication necessitates key agreement between various IoT devices without prior knowledge. Despite existing literature proposing numerous approaches, they exhibit limitations such as low key generation rates and short pairing distances. In this paper, we present InaudibleKey2.0, an inaudible acoustic signal based key generation protocol for mobile devices. Based on acoustic channel reciprocity, InaudibleKey2.0 exploits the acoustic channel frequency response of two legitimate devices as a shared secret for key generation. To significantly enhance performance, InaudibleKey2.0 incorporates novel technologies, including a deep learning-enabled channel prediction model for improved channel reciprocity, a quantization model for increased key generation rates, and a transformer-based reconciliation method for augmented key agreement rates. We conduct comprehensive experiments to evaluate InaudibleKey2.0 in diverse real-world environments. In comparison to state-of-the-art solutions, InaudibleKey2.0 achieves 1.3–9.1 times improvement in key generation rates, 3.2–44 times extension in pairing distances, and 1.2–16 times reduction in information reconciliation counts. Security analysis substantiates that InaudibleKey2.0 is resilient to numerous malicious attacks. Furthermore, we implement InaudibleKey2.0 on modern smartphones and resource-limited IoT devices. The results indicate that it is energy-efficient and can operate on both powerful and resource-limited IoT devices without causing excessive resource consumption.