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Wireless Personal Communications

Enhancement of PLS Model of Massive MIMO by Detecting Eavesdrop Attacks and Improving the Secrecy Capacity of the System Based on Optimization Strategy

Chanchal Soni, Namit Gupta

Published Feb 25, 2023• 6 citations
Publisher
DOI
10.1007/s11277-023-10180-2

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In this paper we study the secrecy performance of a downlink massive multiple-input multiple-output (MIMO) system in the presence of pilot spoofing attack (PSA). Specifically, the base station (BS), which is equipped with low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs), exploits the low-complexity random artificial noise (AN) to improve secrecy. With the aid of the additive quantization noise model, the ergodic secrecy rate is derived. Based on the derived results, we study the impact of ADCs' and DACs' resolutions on the secrecy performance for the two different scenarios: the scenarios with and without the knowledge of the PSA power. The optimal power allocation parameter, which allocates power between the information-bearing signal and AN, is derived for the scenario with the knowledge of the PSA power. For ADCs, our theoretical results indicate that high-resolution ADCs are always favorable for improving secrecy, regardless of the availability of the PSA power. However, the impact of DACs' resolution is related to the knowledge of the PSA power. For the scenario without the PSA power, enhancing the resolution of DACs does not necessarily improve the secrecy performance due to the inappropriate power allocation. For the scenario with the PSA power, DACs with different resolutions can achieve the same ergodic secrecy rate in most cases, since the optimal power allocation can make a balance between mitigating the rate loss due to coarse quantization and injecting AN for anti-eavesdropping. Only in the case where the PSA power is small, high-resolution DACs achieve a higher ergodic secrecy rate. Finally, we propose a PSA detection method and study the effect of low-resolution ADCs on the detection performance. It is found that the resolution of ADCs has little effect on the performance of PSA detection.

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ABSTRACT Millimeter‐Wave (mmWave) massive Multiple Input Multiple Output (MIMO) with Orthogonal Frequency Division Multiplexing (OFDM) is a key enabler of next‐generation wireless communication, providing high spectral efficiency and improved data rates. Massive MIMO utilizes a large number of antennas at the base station to enhance spatial diversity, while OFDM mitigates inter‐symbol interference and enhances spectral efficiency. The combination of mmWave and massive MIMO with OFDM enables high‐capacity communication with reduced latency, making it ideal for 5G and beyond networks. Physical Layer Security (PLS) provides a robust alternative to traditional cryptographic techniques, leveraging information‐theoretic principles to counter advanced eavesdropping threats, including quantum computing attacks. However, conventional PLS key generation methods suffer from reduced secrecy due to the slow variation of channel characteristics. To enhance security, a deep learning‐based key generation approach is proposed using a deep graph convolutional neural network to extract virtual angle of arrival and angle of departure features. The proposed method achieves a bit agreement ratio of 0.5767 and a key generation rate of 1856.59 bits/s, significantly improving key secrecy. Secure data transmission is ensured using a hybrid deep Q‐learning with recurrent neural network framework, incorporating on‐off transmission schemes, which enhances security by achieving a secrecy capacity of 5.37 bps/Hz, maintaining a throughput of 119.55 Mbps, and a latency of 8.00 ms, ensuring scalability. Additionally, evaluations of the secrecy rate of 5.37 bps/Hz, secrecy outage probability of 10 −2 , and 95% Eavesdropper interception capability, demonstrate significant improvements over state‐of‐the‐art techniques.
In this paper we study the secrecy performance of a downlink massive multiple-input multiple-output (MIMO) system in the presence of pilot spoofing attack (PSA). Specifically, the base station (BS), which is equipped with low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs), exploits the low-complexity random artificial noise (AN) to improve secrecy. With the aid of the additive quantization noise model, the ergodic secrecy rate is derived. Based on the derived results, we study the impact of ADCs' and DACs' resolutions on the secrecy performance for the two different scenarios: the scenarios with and without the knowledge of the PSA power. The optimal power allocation parameter, which allocates power between the information-bearing signal and AN, is derived for the scenario with the knowledge of the PSA power. For ADCs, our theoretical results indicate that high-resolution ADCs are always favorable for improving secrecy, regardless of the availability of the PSA power. However, the impact of DACs' resolution is related to the knowledge of the PSA power. For the scenario without the PSA power, enhancing the resolution of DACs does not necessarily improve the secrecy performance due to the inappropriate power allocation. For the scenario with the PSA power, DACs with different resolutions can achieve the same ergodic secrecy rate in most cases, since the optimal power allocation can make a balance between mitigating the rate loss due to coarse quantization and injecting AN for anti-eavesdropping. Only in the case where the PSA power is small, high-resolution DACs achieve a higher ergodic secrecy rate. Finally, we propose a PSA detection method and study the effect of low-resolution ADCs on the detection performance. It is found that the resolution of ADCs has little effect on the performance of PSA detection.
The emerging Internet of Things (IoT) and bidirectional communications in smart grid are expected to improve smart grid capabilities and electricity management. Because of massive number of IoT devices in smart grid, size of the data to be transmitted increases, that demands a high data rate to meet the real-time smart grid communication requirement. Sub-6 GHz, millimeter-wave (mmWave) technologies, and massive multiple-input multiple-output (MIMO) technologies can meet high data rate demands. However, IoT enabled smart grid is still subject to various security challenges such as eavesdropping, where attackers attempt to overhear the transmitted signals and the jamming attack, where the attacker perturbs the received signals at the receiver. In this paper, our goal is to investigate jamming and eavesdropping attacks while improving secrecy capacity for smart grid communications. Specifically, we propose to employ a hybrid beamforming design for wireless communications in smart energy grid. In previous works, the secrecy capacity is increased by randomly augmenting the source power or setting the system combiners. Unlike state-of-the-art, we design and evaluate the Gradient Ascent algorithm to search for the best combiners/waveform that maximizes the secrecy capacity in smart grid communications. We also study two different optimization scenarios by considering both fixed and variable transmit power. Numerical results are used for performance evaluation and supporting our formal analysis.