Deep learning based physical layer security for terrestrial communications in 5G and beyond networks: A survey

Sharma, Himanshu; Kumar, Neeraj

doi:10.1016/j.phycom.2023.102002

Cited by 21 publications

(5 citation statements)

References 123 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%

“…For instance, the authors in [42] survey recent advances in DL-based coding, focusing on enhancing the specific coding method using DL techniques. About the security, the authors in [43] offer a review of DL-based security techniques for addressing issues like attack detection and authentication in 5G and beyond networks. Given the importance of communication delays, authors in [45] discuss the need for real-time DL in the physical layer, summarizing the current advancements and limitations in this area.…”

Section: B Relate Work 1)mentioning

confidence: 99%

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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%

Section: B Relate Work 1)mentioning

confidence: 99%

Effect of heavy metals on duckweed growth

Nguyen¹,

Hoang²,

Pham³

et al. 2022

CTUJS

View full text Add to dashboard Cite

show abstract

“…(8) Weight-updating of the neural network β by the learning parameter Ѓ. (9) end for (10) end for (11) return neural network β ALGORITHM 1: DINN.…”

Section: Candw Attackmentioning

confidence: 99%

“…Moreover, the buyers oppose this kind of replacement technique resulting in higher costs. In the initial stage, these issues are not fxed in [9], and hence they become the primary target for upgrading the system in the core network and there are a lot of ways to fx it. Mostly, the vendors here oppose the replacement at a great cost.…”

Section: Introductionmentioning

confidence: 99%

Secured Wireless Network Based on a Novel Dual Integrated Neural Network Architecture

Ramachandra,

Chavan,

Supreeth

et al. 2023

Journal of Electrical and Computer Engineering

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The development of the fifth generation (5G) and sixth generation (6G) wireless networks has gained wide spread importance in all aspects of life through the network due to their significantly higher speeds, extraordinarily low latency, and ubiquitous availability. Owing to the importance of their users, components, and services to our everyday lives, the network must secure all of these. With such a wide range of devices and service types being present in the 5G ecosystem, security issues are now much more prevalent. Security solutions, are not implemented, must already be envisioned in order to deal with a range of attacks on numerous services, cutting-edge technology, and more user information available over the network. This research proposes the dual integrated neural network (DINN) for secure data transmission in wireless networks. DINN comprises two neural networks based on sparse and dense dimensions. DINN is designed for any presence of deep learning-based attack in a physical security layer. DINN is evaluated considering the various machine learning attack such as basic_iterative_method attack, momentum_iterative_method attack, post_gradient_descent attack, and C&W attack; comparison is carried out on existing and DINN, considering attack success rate and MSE. Performance analysis suggests that DINN holds a higher level of security against the above attacks.

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“…In addition, advancements in cloud computing systems and Graphic Processing Units (GPUs) attract researchers' attention and enable them to monitor urban dynamics in real time [11]. Therefore, these developments in hardware and data transmission provide an opportunity to enhance ITS structures and implement state-of-art methodologies such as deep learning neural networks for vehicle detection [12]. Vehicle detection is a prominent stage of ITS construction [1,13].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Multi-Label Deep Learning Classifiers for Real-Time Vehicle Detection to Support Intelligent Transportation Systems

Shokri,

Larouche,

Homayouni

2023

Smart Cities

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An Intelligent Transportation System (ITS) is a vital component of smart cities due to the growing number of vehicles year after year. In the last decade, vehicle detection, as a primary component of ITS, has attracted scientific attention because by knowing vehicle information (i.e., type, size, numbers, location speed, etc.), the ITS parameters can be acquired. This has led to developing and deploying numerous deep learning algorithms for vehicle detection. Single Shot Detector (SSD), Region Convolutional Neural Network (RCNN), and You Only Look Once (YOLO) are three popular deep structures for object detection, including vehicles. This study evaluated these methodologies on nine fully challenging datasets to see their performance in diverse environments. Generally, YOLO versions had the best performance in detecting and localizing vehicles compared to SSD and RCNN. Between YOLO versions (YOLOv8, v7, v6, and v5), YOLOv7 has shown better detection and classification (car, truck, bus) procedures, while slower response in computation time. The YOLO versions have achieved more than 95% accuracy in detection and 90% in Overall Accuracy (OA) for the classification of vehicles, including cars, trucks and buses. The computation time on the CPU processor was between 150 milliseconds (YOLOv8, v6, and v5) and around 800 milliseconds (YOLOv7).

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Deep learning based physical layer security for terrestrial communications in 5G and beyond networks: A survey

Cited by 21 publications

References 123 publications

Effect of heavy metals on duckweed growth

Effect of heavy metals on duckweed growth

Secured Wireless Network Based on a Novel Dual Integrated Neural Network Architecture

A Comparative Analysis of Multi-Label Deep Learning Classifiers for Real-Time Vehicle Detection to Support Intelligent Transportation Systems

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