Design and Implementation of Traffic Generation Model and Spectrum Requirement Calculator for Private 5G Network

Kim, Daegyeom; Ko, Myeongjin; Kim, Sung-Hyun; Moon, Soo Young; Cheon, Kyung-yul; Park, Seungkeun; Kim, Yunbae; Yoon, Ho‐Geun; Choi, Yong-Hoon

doi:10.1109/access.2022.3149050

Cited by 14 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The next stage of congestion control then begins. If the user equipment (UE) does not initiate any congestion events, the slow-start procedure of the congestion control algorithm causes the congestion window to grow exponentially until the slow-start threshold is reached [34].…”

Section: Models and Methodsmentioning

confidence: 99%

Congestion Analysis of Transport Layer in a Multicell 5G DL Communication System

Andras

Barb

Alexa

et al. 2023

Sensors

View full text Add to dashboard Cite

The fifth generation (5G) marks an important advance in mobile network capabilities. When it comes to high data rates, capacity, spectrum efficiency, and availability, 5G mobile broadband goes above and beyond what was previously possible with standard mobile broadband. The construction of 5G networks is still in the planning stages. These 5G networks will create intelligent networked communication environments by connecting people, things, data, applications, and transport networks. Mobile networks have made it possible for customers’ mobile devices (such as smartphones, tablets, laptops, and so on) to connect to the internet. A variety of distinct protocols will be required to take into consideration the various aspects that 5G possess. One of these is the transport protocol, which is intended to deliver extremely high data transfer rates up to 400 Gbps. The transmission control protocol (TCP) is one of the numerous protocols that are necessary for supporting 5G’s many capabilities. Our work focuses on the detection and analysis, on the downlink (DL) side, of the congestion of the transport layer in single- and multicell environments. For the purpose of the analysis, the following metrics were analyzed: physical resource blocks (PRBs), user throughput, cell throughput, cell edge user throughput, and delay. The work emphasizes the activation of the TCP slow-start algorithm using file transfer protocol (FTP) model two according to 3GPP standards.

show abstract

Section: Models and Methodsmentioning

confidence: 99%

Congestion Analysis of Transport Layer in a Multicell 5G DL Communication System

Andras

Barb

Alexa

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…5G networks that are currently in trend have also implemented deep generative models as in the study presented by Daegyeom Kim et al [86]. It includes a 5G traffic modelling approach as well as a strategy for determining the required spectrum for privatised 5G networks.…”

Section: Deep Generative Modelsmentioning

confidence: 99%

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Gupta

Johri

Srinivasan

et al. 2022

Sensors

View full text Add to dashboard Cite

Today’s advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.

show abstract

“…5G networks that are currently in trend have also implemented deep generative models [9]. It includes a 5G traffic modelling approach as well as a strategy for determining the required spectrum for privatized 5G networks.…”

Section: Public Network Voice Supervisionmentioning

confidence: 99%

Exploring Artificial Intelligence for Network Security: A Case Study of Malware Defence

Badarch¹

2022

AJCST

View full text Add to dashboard Cite

AI has many applications in network security. Network security is one of the most challenging situations. The paper carries out AI based network security analysis and prevention ways of the deep learning models in the network security. We focus on some specific AI applications including voice supervision of public network, malicious code monitoring, smartphone intrusion monitoring, HTTP security monitoring, mobile phone malicious APK code monitoring are bringing the solutions for network security. We studied there are powerful methods such as mobile phone malicious APR code monitoring employed Artificial Neural Network (ANN) model which detects and mitigates predictable and unpredictable DDoS attacks (TCP, UDP, and ICMP protocols). HTTP is running over TCP, then the web server can face many TCP-related attacks, therefore, we have an experiment of HTTP security monitoring, mobile phone malicious APK code monitoring. This paper presents a potential security threats from malicious uses of AI, and proposes ways to better prevent, and mitigate these threats. When planning HTTP service protection, we present it is important to keep in mind that the attack surface is much broader than just the HTTP protocol. We suggest promising areas for further research that could expand the AI based solutions for development of cloud computing-related technologies, and the combination of cloud computing and deep learning technology in the security area.

show abstract

Design and Implementation of Traffic Generation Model and Spectrum Requirement Calculator for Private 5G Network

Cited by 14 publications

References 34 publications

Congestion Analysis of Transport Layer in a Multicell 5G DL Communication System

Congestion Analysis of Transport Layer in a Multicell 5G DL Communication System

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Exploring Artificial Intelligence for Network Security: A Case Study of Malware Defence

Contact Info

Product

Resources

About