Split-learning-based Virtual Physically Unclonable Functions (VPUFs) in Internet of Things (IoT) networks remain vulnerable to eavesdropping and replay attacks due to insufficient security mechanisms that balance robustness with computational efficiency. This paper proposes a novel digital watermarking approach to improve the security of Split-Learning-based VPUFs. The suggested framework utilizes deep learning-based approaches to generate a watermark to be embedded in the latent representation of the VPUF response to provide additional security against eavesdropping and replay attacks without incurring significant hardware or computational overhead. Watermark embedding is done by simulating Rayleigh fading through Jake’s Model to get the secret channel information, which is input to an autoencoder to create a strong latent representation. The formed latent watermark is embedded into the latent response of the VPUF. Experimental testing demonstrates that fidelity remains high under test conditions, reliability, and unforgability, confirming that the watermarking process does not compromise the VPUF’s performance. Further, the proposal supports dual-factor authentication through simultaneous verification of the extracted watermark and the retrieved latent response. This research not only enhances the strength and security of the baseline VPUF mechanism but also provides a cost-effective, scalable solution specifically designed for resource-constrained IoT networks.
Khan et al. (Wed,) studied this question.