Machine learning-based physical layer security: techniques, open challenges, and applications

Kamboj, Anil Kumar; Jindal, Poonam; Verma, Pankaj

doi:10.1007/s11276-021-02781-1

Cited by 24 publications

(11 citation statements)

References 160 publications

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“…2) AI Enabled Physical Layer Communications: AI models are crucial for advancing physical layer communications, spurring numerous research surveys. These studies primarily concentrate on the application of DL in various domains, including signal detection and compression [40], coding [6], [41], [42], security [43], [44], and communication delay [45]. For instance, the authors in [42] survey recent advances in DL-based coding, focusing on enhancing the specific coding method using DL techniques.…”

Section: B Relate Work 1)mentioning

confidence: 99%

Effect of heavy metals on duckweed growth

Nguyen¹,

Hoang²,

Pham³

et al. 2022

CTUJS

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In this study, three clones of the duckweed genus Lemna, collected from three different regions of Vietnam Binh Thuan Bac Giang and Ben Tre were used to evaluate the growth and development in a culture medium containing different concentrations of As3+, Cd2+, or Pb2+. During 14 days of incubation, the growth rate was determined daily by measuring the surface area of all plants of each sample. Experimental results show that the three Lemna clones could grow in nutrient medium contaminated by heavy metal at a concentration of 0.3-0.5 mg/L for As3+, 0.15-0.3 mg/L for Cd2+, and 0.15 mg/L for Pb2+. At these concentrations, the division rate of the treated clones was several times higher than that of the controls. The highest increase in the surface area was recorded for Lemna BTN (68.47 times higher when cultured in medium supplemented in 0.3 mg/L Pb2+ than under control conditions). The surface area of Lemna BTR cultured in a medium supplemented with 0.3 mg/L As3+ was 54.65 times higher than in medium without arsenic. The obtained results showed that Lemna BGG and Lemna BTR can tolerate arsenic and cadmium pollution while Lemna BTN tolerates lead pollution.

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Section: B Relate Work 1)mentioning

confidence: 99%

Effect of heavy metals on duckweed growth

Nguyen¹,

Hoang²,

Pham³

et al. 2022

CTUJS

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“…The integration of ML and PLS has also enticed some research interest. Kamboj et al [ 10 ] reviewed physical layer authentication, antenna selection, relay node selection, and integration with ML, and Jiang [ 30 ] presented reviews of PLA schemes using machine learning for the 5G-based IoT. They also compared PLA schemes.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…Machine learning (ML) is a subset of artificial intelligence (AI) that emerged from pattern recognition [ 10 ]. Lately, research in wireless communication has noted the distinction and effectiveness of machine learning by identifying the probability of learning based on signal classification [ 11 ] and specific emitter identification [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches

Alhoraibi

Alghazzawi

Alhebshi

et al. 2023

Sensors

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The physical layer security of wireless networks is becoming increasingly important because of the rapid development of wireless communications and the increasing security threats. In addition, because of the open nature of the wireless channel, authentication is a critical issue in wireless communications. Physical layer authentication (PLA) is based on distinctive features to provide information-theory security and low complexity. However, although many researchers are interested in the PLA and how it might be used to improve wireless security, there is surprisingly little literature on the subject, with no systematic overview of the current state-of-the-art PLA and the main foundations involved. Therefore, this paper aims to determine and systematically compare existing studies in the physical layer authentication. This study showed whether machine learning approaches in physical layer authentication models increased wireless network security performance and demonstrated the latest techniques used in PLA. Moreover, it identified issues and suggested directions for future research. This study is valuable for researchers and security model developers interested in using machine learning (ML) and deep learning (DL) approaches for PLA in wireless communication systems in future research and designs.

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“…Physical layer security (PLS) has emerged as a new set of techniques employed at the physical layer to either strengthen the high level cryptographic security schemes, or to alleviate their computational overhead [26]- [28]. 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.…”

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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Machine learning-based physical layer security: techniques, open challenges, and applications

Cited by 24 publications

References 160 publications

Effect of heavy metals on duckweed growth

Effect of heavy metals on duckweed growth

Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches

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

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