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ACM Workshop on Wireless Security and Machine Learning (WiseML)

Machine Learning-Assisted Wireless PHY Key Generation with Reconfigurable Intelligent Surfaces

Long Jiao, Guohua Sun, Junqing Le, Kai Zeng

Published Jun 21, 2021• 29 citations
Publisher

Abstract

The key generation rate (KGR) performance of wireless physical layer (PHY) key generation can be limited by the quasi-static slow fading environment. In this work, we aim to exploit the radio environment reconfiguration ability enabled by reconfigurable intelligent surface (RIS) to improve KGR of PHY key generation. By rapidly changing the RIS configurations, the randomness or entropy rate of the wireless channel can be significantly increased, thus improving the KGR. To achieve high KGR while keeping low bit disagreement ratio (BDR), for the first time, we propose a machine learning (ML) based adaptive quantization level prediction scheme to decide an optimal quantization level based on channel state information (CSI). Simulation results show that with a prediction accuracy as high as 98.2%, the proposed ML-based prediction model tends to assign high quantization levels in the high SNR regime to reduce BDR, while adopting low quantization levels under low SNRs to maintain a low BDR.

DOI
10.1145/3468218.3469042

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IEEE Wireless Communications Letters•Jun 10, 2024•3 citations
The key generation rate (KGR) performance of wireless physical layer (PHY) key generation can be limited by the quasi-static slow fading environment. In this work, we aim to exploit the radio environment reconfiguration ability enabled by reconfigurable intelligent surface (RIS) to improve KGR of PHY key generation. By rapidly changing the RIS configurations, the randomness or entropy rate of the wireless channel can be significantly increased, thus improving the KGR. To achieve high KGR while keeping low bit disagreement ratio (BDR), for the first time, we propose a machine learning (ML) based adaptive quantization level prediction scheme to decide an optimal quantization level based on channel state information (CSI). Simulation results show that with a prediction accuracy as high as 98.2%, the proposed ML-based prediction model tends to assign high quantization levels in the high SNR regime to reduce BDR, while adopting low quantization levels under low SNRs to maintain a low BDR.
In quasi-static environments, the key generation rate (KGR) of wireless physical layer key generation (PLKG) can be greatly limited. In this paper, we aim to improve the KGR by reconfigurable intelligent surface (RIS) and jointly design the quantizer and phase shift matrix. We give a reconfiguration strategy able to quantify both real and imaginary parts at the same time and give its closed-form solution of the phase shift matrix. To reduce the bit disagreement ratio (BDR), we design a quantizer suitable for RIS-assisted PLKG which uses non-uniform quantization. To achieve high KGR while maintain low BDR, we analyze the BDR in different quantization levels and realize an adaptive quantization selection. Meanwhile, the simulation results verify that the KGR of the proposed scheme is more than twice that of the existing scheme under a certain BDR threshold.
Abstract In quasi-static environment, the performance of physical-layer key generation (PKG) is limited by low entropy. Deploying reconfigurable intelligent surface (RIS) is a mainstream method to solve this problem. However, the secret key performance (SKP) is generally insufficient at low signal-to-noise ratio (SNR). In this paper, we propose an RIS-assisted adaptive PKG scheme. Specifically, we design phase based uniform quantization to enhance the randomness of the RIS channel while simplifying computations. Then, we present an RIS phase shift matrix design based on rejection sampling to adapt the quantization algorithm, which can reduce bit disagreement rate (BDR) and improve key generation rate (KGR). Furthermore, to balance the system’s requirements for low BDR and high KGR, we derive a theoretical model for estimating the BDR probability. By computing the theoretical BDR, we determine the maximum quantization order for different SNRs. Combined with a discard strategy, this enables adaptive selection of the quantization order. Simulation results demonstrate that the proposed scheme achieves lower BDR and higher KGR than existing approaches. Moreover, it does not require complex algorithms, making it suitable for resource-constrained internet of things scenarios. (RIS_Assist_PKG: https://github.com/GQQ1206/RIS_Assist_PKG )
In quasi-static wireless channel scenarios, the generation of physical layer keys faces the challenge of invariant spatial and temporal channel characteristics, resulting in a high key disagreement rate (KDR) and low key generation rate (KGR). To address these issues, we propose a novel reconfigurable intelligent surface (RIS)-aided secret key generation approach using an autoencoder and K -means quantization algorithm. The proposed method uses channel state information (CSI) for channel estimation and dynamically adjusts the reflection coefficients of the RIS to create a rapidly fluctuating channel. This strategy enables the extraction of dynamic channel parameters, thereby enhancing channel randomness. Additionally, by integrating the autoencoder with the K -means clustering quantization algorithm, the method efficiently extracts random bits from complex, ambiguous, and high-dimensional channel parameters, significantly reducing KDR. Simulations demonstrate that, under various signal-to-noise ratios (SNRs), the proposed method performs excellently in terms of KGR and KDR. Furthermore, the randomness of the generated keys is validated through the National Institute of Standards and Technology (NIST) test suite.
Physical layer key generation (PKG) is a promising means to provide on-the-fly shared secret keys by exploiting the intrinsic randomness of the radio channel. However, the performance of PKG is highly dependent on the propagation environments. Due to its feature of controlling the wireless environment, reconfigurable intelligent surface (RIS) is appealing to be applied in PKG. In this article, in contrast to the existing literature, we investigate both the constructive and destructive effects of RIS on the PKG scheme. For the constructive aspect, we have identified static and wave-blockage environments as two RIS-empowered-PKG applications in future wireless systems. In particular, our experimental results in a static environment showed that RIS can enhance the entropy of the secret key, achieving a key generation rate (KGR) of 97.39 bit/s with a bit disagreement rate (BDR) of 0.083. In multi-user systems where some remote users are in worse channel conditions, the proposed RIS-assisted PKG algorithm improves the sum secret key rate by more than 2 dB, compared to the literature. Furthermore, we point out that RIS could be utilized by an attacker to perform new jamming and leakage attacks and give countermeasures, respectively. Finally, we outline future research directions for PKG systems in light of the RIS.
This paper proposes a reconfigurable intelligent surface (RIS) assisted physical layer key generation (PKG) scheme to increase the key generation rate (KGR). Firstly, we provide a high-rate key generation protocol that rapidly and randomly switches the phase of RIS elements. Then, we analyze the key information leaked to passive eavesdroppers and give the closed-form expression of KGR when the number of RIS elements is sufficiently large. Monte Carlo simulation and numerical results show that our proposed scheme can boost KGR in a static environment.
The key generation rate (KGR) of physical layer key generation (PLKG) is severely restricted in quasi-static environments due to a lack of entropy. To address the problem, a reconfigurable intelligent surface (RIS) is introduced. In this letter, we consider the design of phase shift matrix with vector quantization to fully exploit the randomness of the RIS. In order to reduce the bit disagreement ratio (BDR), we design a vector quantizer to fully exploit the quantization range of the RIS channel. Additionally, we provide the design strategy for the phase shift matrix that aligns with the vector quantizer. Furthermore, we present a closed-form solution for the phase shift matrix. Meanwhile, to balance the tradeoff between high KGR and low BDR, we calculate the BDR in different quantization orders and propose an adaptive quantization order selection. Simulations validate that our proposed scheme obtain a higher KGR than the existing schemes under a certain BDR threshold.