Secret-Key Agreement Through Hidden Markov Modeling of Wavelet Scattering Embeddings

TL;DR

This work reframes secret-key generation for IoT by exploiting statistical channel reciprocity rather than relying on instantaneous CSI similarity. It introduces wavelet scattering network–based CSI feature representations that are robust to time-warping, noise, and fading, and uses dimensionality reduction to reveal reciprocal cluster structures that form hidden Markov model states for key generation. The AP and STA independently build HMMs from their reciprocal clusters, generate keys from state sequences, and achieve a fivefold increase in key-generation rate with no quantization errors. Evaluations on WiFi IoT hardware demonstrate significant gains in throughput and reliability, highlighting the approach's practicality for secure, low-cost wireless devices.

Abstract

Secret-key generation and agreement based on wireless channel reciprocity offers a promising avenue for securing IoT networks. However, existing approaches predominantly rely on the similarity of instantaneous channel measurement samples between communicating devices. This narrow view of reciprocity is often impractical, as it is highly susceptible to noise, asynchronous sampling, channel fading, and other system-level imperfections -- all of which significantly impair key generation performance. Furthermore, the quantization step common in traditional schemes introduces irreversible errors, further limiting efficiency. In this work, we propose a novel approach for secret-key generation by using wavelet scattering networks to extract robust and reciprocal CSI features. Dimensionality reduction is applied to uncover hidden cluster structures, which are then used to build hidden Markov models for efficient key agreement. Our approach eliminates the need for quantization and effectively captures channel randomness. It achieves a 5x improvement in key generation rate compared to traditional benchmarks, providing a secure and efficient solution for key generation in resource-constrained IoT environments.

Figures (9)

• Figure 1: Overview of the proposed secret-key generation approach.
• Figure 2: CSI data distortion at AP and STA impacting CSI reciprocity.
• Figure 3: CSI feature representations using WSN: from input to 2nd-order scattering features
• Figure 4: Pearson's correlation of scattering features.
• Figure 5: Cluster sets within CSI scattering feature embedding of a single scattering feature representation. The embeddings are for the scattering representations of $9000$ CSI samples of subcarriers $40$ and $50$ of WiFi at two timestamps T1 and T2.