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IEEE Wireless Communications Letters

Secret Key Generation With Intelligent Reflecting Surface Under the Pilot Contamination Attack

Enjun Xia, Bin‐Jie Hu, Qiaoqiao Shen

Published Oct 19, 2023• 5 citations
Publisher

Abstract

Secret key generation (SKG) is a promising approach to improving communication security, but it faces many attacks such as eavesdropping and pilot contamination. This study investigates intelligent reflecting surface (IRS) assisted SKG under the pilot contamination attack. First, we acquire the lower bound of the secret key rate on the basis of our system model. Next, an optimization problem of the IRS’s reflecting coefficients is formulated to maximize the lower bound. It is challenging to solve it due to the non-convex quadratic and quartic constraints. We, therefore, propose an efficient algorithm to solve it on the basis of successive convex approximation. Numerical results demonstrate that the IRS can improve the lower bound to counter the pilot contamination attack, and the increase in the correlation coefficient between legitimate and eavesdropping channels does not always decrease the lower bound. Additionally, the secret key rate decreases as Eve’s transmitting power increases.

DOI
10.1109/lwc.2023.3325361

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Secret key generation (SKG) is a promising approach to improving communication security, but it faces many attacks such as eavesdropping and pilot contamination. This study investigates intelligent reflecting surface (IRS) assisted SKG under the pilot contamination attack. First, we acquire the lower bound of the secret key rate on the basis of our system model. Next, an optimization problem of the IRS’s reflecting coefficients is formulated to maximize the lower bound. It is challenging to solve it due to the non-convex quadratic and quartic constraints. We, therefore, propose an efficient algorithm to solve it on the basis of successive convex approximation. Numerical results demonstrate that the IRS can improve the lower bound to counter the pilot contamination attack, and the increase in the correlation coefficient between legitimate and eavesdropping channels does not always decrease the lower bound. Additionally, the secret key rate decreases as Eve’s transmitting power increases.
Physical layer secret key generation (SKG) is a promising supplement for high-level encryption, which leverages the channel reciprocity and the natural randomness of wireless channel. However, practical implementations often exhibit static environment. To solve this problem, this paper proposes a novel secret key generation scheme assisted by an intelligent reflecting surface (IRS) which can smartly reconfigure the radio signal propagation to improve the secret key rate of SKG. Specially, we formulate the minimum achievable secret key capacity in the IRS-assisted multiple-input single-output (MISO) system with multiple eavesdroppers. Then, an optimization framework for designing IRS reflecting coefficients based on the secret key capacity lower bound is proposed. Numerical results show that our optimal IRS-assisted SKG scheme can achieve much higher achievable secret key capacity when compared with two benchmark schemes.
We propose and analyze secret key generation using intelligent reflecting surface (IRS) assisted wireless communication networks. To this end, we first formulate the minimum achievable secret key capacity for an IRS acting as a passive beamformer in the presence of multiple eavesdroppers. Next, we develop an optimization framework for the IRS reflecting coefficients based on the secret key capacity lower bound. To derive a tractable and efficient solution, we design and analyze a semidefinite relaxation (SDR) and successive convex approximation (SCA) based algorithm for the proposed optimization. Simulation results show that employing our IRS-based algorithm can significantly improve the secret key generation capacity for a wide-range of wireless channel parameters.
We propose and analyze secret key generation using intelligent reflecting surface (IRS) assisted wireless communication networks. To this end, we first formulate the minimum achievable secret key capacity for an IRS acting as a passive beamformer in the presence of multiple eavesdroppers. Next, we develop an optimization framework for the IRS reflecting coefficients based on the secret key capacity lower bound. To derive a tractable and efficient solution, we design and analyze a semidefinite relaxation (SDR) and successive convex approximation (SCA) based algorithm for the proposed optimization. Simulation results show that employing our IRS-based algorithm can significantly improve the secret key generation capacity for a wide-range of wireless channel parameters.
Physical-layer key generation (PKG) can generate symmetric keys between two communication ends based on the reciprocal uplink and downlink channels. By smartly reconfiguring the radio signal propagation, intelligent reflecting surface (IRS) is able to improve the secret key rate of PKG. However, existing works involving IRS-assisted PKG are concentrated in single-antenna wireless networks. So this paper investigates the problem of PKG in the IRS-assisted multiple-input single-output (MISO) system, which aims to maximize the secret key rate by optimally designing the IRS passive beamforming. First, we analyze the correlation between channel state information (CSI) of eavesdropper and legitimate ends and derive the expression of the upper bound of secret key rate under passive eavesdropping attack. Then, an optimal algorithm for designing IRS reflecting coefficients based on Semi-Definite Relaxation (SDR) and Taylor expansion is proposed to maximize the secret key rate. Numerical results show that our optimal IRS-assisted PKG scheme can achieve much higher secret key rate when compared with two benchmark schemes.
Reconfigurable intelligent surface (RIS) has been widely studied as a promising approach in improving secret key rate (SKR) for physical layer (PHY) secret key generation (SKG). Existing RIS-assisted PHY SKG studies mainly focus on improving the SKR by optimizing RIS phase. However, RIS can also be exploited by active attackers to contaminate the channel probing. As a result, there is a trade-off between the increased randomness and the improved correlation between legitimate channels and eavesdropping channels in a RIS-assisted SKG system under channel contamination. In this paper, we study the impact of RIS on PHY SKG by analyzing the SKR performance. A closed-form expression for the lower-bound of secret key capacity is obtained. Theoretical analysis shows that gains of legitimate channel, eavesdropping channel, and RIS-induced channels are key parameters in determining whether the assisting RIS is beneficial or harmful to the PHY SKG. Further, a SKR maximization problem under channel contamination is proposed and solved. Results show that the assisting RIS is definitely harmful to the SKG when the gain ratio of legitimate channel and eavesdropping channel is reduced with the leverage of RIS, while it can significantly improve the SKR with an optimized RIS phase when the gain ratio is improved with the help of RIS.
Physical layer key generation (PLKG) can significantly enhance the security of classic encryption schemes by enabling them to change their secret keys significantly faster and more efficient. However, due to the reliance of PLKG techniques on channel medium, reaching a high secret key rate is challenging in static environments. Recently, exploiting intelligent reflecting surface (IRS) as a means to induce randomness in static wireless channels has received significant research interest. However, the impact of spatial correlation between the IRS elements is rarely studied. To be specific, for the first time, in this contribution, we take into account a spatially correlated IRS which intends to enhance the secret key generation (SKG) rate in a static medium. Closed form analytical expressions for SKG rate are derived for the two cases of random phase shift and equal random phase shift for all the IRS elements. We also analyze the temporal correlation between the channel samples to ensure the randomness of the generated secret key sequence. We further formulate an optimization problem in which we determine the optimal portion of time within a coherence interval dedicated for the direct and indirect channel estimation. We show the accuracy and the fast convergence of our proposed sequential convex programming (SCP) based algorithm and discuss the various parameters affecting spatially correlated IRS assisted PLKG.