Deep Learning Enabled Physical Layer Security to Combat Eavesdropping in Massive MIMO Networks

Siyad, C. Ismayil; Tamilselvan, S.

doi:10.1007/978-3-030-37051-0_72

Cited by 8 publications

(2 citation statements)

References 13 publications

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“…Some of the security issues have been explored in [277]- [281]. In [278], the effect of adversarial attack and jamming attack on CSI feedback in DL-based mMIMO were investigated, while in [279], [280] spoofing attack and eavesdropper's attacks in [281] were investigated on the PHY. Although the use of the ML is advocated to mitigate security threats, however, a major issue pointed out in [35] is the delay in the detection of such threats.…”

Section: Securitymentioning

confidence: 99%

Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

et al. 2022

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Machine learning (ML) which is a subset of artificial intelligence is expected to unlock the potential of challenging large-scale problems in conventional massive multiple-input-multiple-output MIMO (CM-MIMO) systems. This introduces the concept of intelligent mMIMO (I-mMIMO) systems. Due to the surge of application of different ML techniques in the enhancement of mMIMO systems for existing and emerging use cases beyond fifth-generation (B5G) networks, this article aims to provide an overview of the different aspects of the I-mMIMO systems. First, the characteristics and challenges of the CM-MIMO have been identified. Secondly, the most recent efforts aimed at applying ML to a different aspect of CM-MIMO systems are presented. Thirdly, the deployment of I-mMIMO and efforts towards standardization are discussed. Lastly, the future trends of I-mMIMO enabled application systems are presented. The aim of this paper is to assist the readers to understand different ML approaches in CM-MIMO systems, explore some of the advantages and disadvantages, identify some of the open issues, and motivate the readers toward future trends.

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Section: Securitymentioning

confidence: 99%

Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

et al. 2022

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“…PLS schemes, many of which make use of ML algorithms, can be classified into three domains: channel, signal, and coding-based [29]. A DL-based PLS scheme that predicts the channel coefficients between the legitimate parties is proposed in [30], and a comparison between the proposed scheme and zero forcing based beamforming for mMIMO channels in terms of the secrecy rate and the secrecy outage probability is provided. A trade-off between DL-aided PLS and reliability in terms of the intercept probability and the outage probability is presented in [31].…”

Section: Introductionmentioning

confidence: 99%

Secure Initial Access and Beam Alignment Using Deep Learning in 5G and Beyond Systems

Ozmat,

Yazici,

Demirkol

2024

IEEE Access

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5G and beyond networks will require fast, energy efficient, and secure initial access. In this study, a deep learning-based secure initial beam selection method is proposed that ranks the beam pairs between a transmitter and a legitimate user aiming to maximize the signal strength the user receives, while keeping the signal strength that the eavesdropper sees below a threshold. Instead of an exhaustive search, the initial beam selection is performed over a limited number of the top beam pairs, leading to reduced communication overhead and energy consumption. The proposed scheme is evaluated using data obtained from a real-life mobile network topology as well as a synthetic data set based on the same geographical site but with statistical system-level environment variables. Numerical results show that the signalling overhead can be reduced by 75% with 99.66% accuracy in terms of the best beam pair, and 99.89% of the achievable signal strength. In terms of security, the proposed method has been shown to improve secure coverage probability by 68.12% compared to the best-coverage beam selection scenario.INDEX TERMS 5G, physical layer security, beam management, mMIMO, NR SSB beam sweeping, deep learning, DNN.Recently, machine learning (ML), especially deep learning (DL), has become a prominent technique in a wide range of research areas for mobile and wireless networks. Potential areas of DL include mobility analysis, user localization, wireless sensor networks, network control, network security, signal processing, network-level and application-level mobile data analysis [5]. Specifically, DL has been proposed to be used in the beam management processes. It excels at extracting nonlinear features in angular and time domains, ensuring highly accurate beam pair prediction. Additionally, it has been proposed for predictive beam switching to mini-

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Soft Computing Techniques for Physical Layer Security of IoT Devices

Siyad

Tamilselvan

2021

Studies in Big Data

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Deep Learning Enabled Physical Layer Security to Combat Eavesdropping in Massive MIMO Networks

Cited by 8 publications

References 13 publications

Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

Secure Initial Access and Beam Alignment Using Deep Learning in 5G and Beyond Systems

Soft Computing Techniques for Physical Layer Security of IoT Devices

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