Market Dynamics and Security Considerations of 5G

Prasad, Anand R.; Lakshminarayanan, Sivakamy; Arumugam, Sivabalan

doi:10.13052/jicts2245-800x.532

Cited by 3 publications

(5 citation statements)

References 23 publications

(19 reference statements)

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“…The amount of information may become even larger if other critical factors are considered, such as cyber-security indicators [1], or in case network monitoring data [5] are directly put as an input to the forecast model. These complex models with a wide variety of features are the rationale behind the so-called big data trend, recently emerged in both the scientific community [22] and the ICT market [4,13]. Mapping this kind of big data structures into intelligible models is a formidable problem.…”

Section: Related Workmentioning

confidence: 99%

A Hardware Acceleration Platform for AI-Based Inference at the Edge

Karras

Pallis

Mastorakis

et al. 2019

Circuits Syst Signal Process

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Machine learning (ML) algorithms are already transforming the way data are collected and processed in the data center, where some form of AI has permeated most areas of computing. The integration of AI algorithms at the edge is the next logical step which is already under investigation. However, harnessing such algorithms at the edge will require more computing power than what current platforms offer. In this paper, we present an FPGA system-on-chip-based architecture that supports the acceleration of ML algorithms in an edge environment. The system supports dynamic deployment of ML functions driven either locally or remotely, thus achieving a remarkable degree of flexibility. We demonstrate the efficacy of this architecture by executing a version of the well-known YOLO classifier which demonstrates competitive performance while requiring a reasonable amount of resources on the device. Keywords Acceleration • YOLO • EDGE • Fog • ML • Acceleration of machine learning • AI • Computing • PCP Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Section: Related Workmentioning

confidence: 99%

A Hardware Acceleration Platform for AI-Based Inference at the Edge

Karras

Pallis

Mastorakis

et al. 2019

Circuits Syst Signal Process

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“…This section presents the architecture, technology and security aspects of 5G that would cater for the Tele-healthcare use cases and applications presented in Section 7.2. The reader may find more information about 5G network domains, architectures and slicing in [22] and [23], respectively.…”

Section: G Communication Systemmentioning

confidence: 99%

“…The most likely initial deployment options are illustrated in Figure 7.5 a) and c) (see e.g. [22] and [23]).…”

Section: G Reference Architecturementioning

confidence: 99%

“…The CU-DU split architecture can provide on-demand deployment [22,23]: The CU may run on a cloud platform and can be deployed in different locations to meet the efficiency-latency trade-off for offering different type of services, e.g. the latency in regional, local and access data centre deployment scenarios is below 50 ms, 20 ms and 5 ms, respectively.…”

Section: G Reference Architecturementioning

confidence: 99%

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5G Communication Systems and Connected Healthcare

Soldani¹,

Innocenti²

2019

Enabling 5G Communication Systems to Support Vertical Industries

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This chapter focuses on 5G mobile systems for ultra-reliable low latency communications applied to two specific healthcare use cases: Wireless Tele Surgery (WTS), using a mobile console and a robotic platform with video, audio and haptic feedback; and Wireless Service Robot (WSR), a companion robot or service robot performing tasks of social caretakers, professional personnel or family members. Cyber-sickness and latency between the robotic platform and a reasoning server are the new technical problems the 5G mobile system is expected to solve, e.g. in hospices, hospitals, homes and campus areas.In the following sections, we first present the use cases and related technical requirements; then, we describe the 5G reference architecture and key enabling technologies to meet the performance, dependability and security targets for Tele-healthcare. This is followed by a comprehensive analysis of the costs and revenues operators and healthcare providers would encounter to deliver the corresponding services, if a 5G system was deployed.

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“…The results obtained from the primary and secondary data show that there are some important cybersecurity issues related to the use of 4G and 5G telecommunication networks that need to be considered by the regulators. The result was compared with the results of previous work reported in [13,14] for validation. The intrusion detection rate in the proposed approach can be varied according to the number of nodes that arrived in the network.…”

Section: Introductionmentioning

confidence: 96%

Cyber-Attack Detection and Mitigation Using SVM for 5G Network

Alshunaifi¹,

Mishra²,

Alshehri³

2022

Intelligent Automation &Amp; Soft Computing

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5G technology is widely seen as a game-changer for the IT and telecommunications sectors. Benefits expected from 5G include lower latency, higher capacity, and greater levels of bandwidth. 5G also has the potential to provide additional bandwidth in terms of AI support, further increasing the benefits to the IT and telecom sectors. There are many security threats and organizational vulnerabilities that can be exploited by fraudsters to take over or damage corporate data. This research addresses cybersecurity issues and vulnerabilities in 4G (LTE) and 5G technology. The findings in this research were obtained by using primary and secondary data. Secondary data was collected by reviewing literature and conducting surveys. Primary data were obtained by conducting an experimental simulation using the support vector machine (SVM) approach. The results show that cybersecurity issues related to 4G and 5G need to be addressed to ensure integrity, confidentiality, and availability. All enterprises are constantly exposed to a variety of risks. Also implemented an efficient SVM-based attack detection and mitigation system for 5G network. The proposed intrusion detection system defends against security attacks in the 5G environment. The results show that the throughput and intrusion detection rate is higher while the latency, energy consumption, and packet loss ratio are low, indicating that the proposed intrusion detection and defense system has achieved better QoS. The security solutions are fast and effective in detecting and mitigating cyber-attacks.

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Market Dynamics and Security Considerations of 5G

Cited by 3 publications

References 23 publications

A Hardware Acceleration Platform for AI-Based Inference at the Edge

A Hardware Acceleration Platform for AI-Based Inference at the Edge

5G Communication Systems and Connected Healthcare

Cyber-Attack Detection and Mitigation Using SVM for 5G Network

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