On the ICN-IoT with federated learning integration of communication: Concepts, security-privacy issues, applications, and future perspectives

Rahman, Asheq; Hasan, Kamrul; Kundu, Dipanjali; Islam, Md. Jahidul; Debnath, Tanoy; Band, Shahab S.; Kumar, Neeraj

doi:10.1016/j.future.2022.08.004

Cited by 59 publications

(16 citation statements)

References 139 publications

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“…These terms are connected due to recent research focused on the use of deep learning reinforcement algorithms to improve the allocation of resources in the network and thus guarantee a better special distribution of users; this study is supported by one of the techniques used to modulate the signal and thus appropriately assign the characteristics to the signal according to the needs [77]. Additionally, as revealed in the quadrants of the previous figure, the use of artificial intelligence technology in the latest-generation networks is aimed mainly at guaranteeing the adequate availability of resources due to the massive number of IoT devices that demand considerable resources [78]. Finally, the terms "anomalies", "computer architecture" and The above network of keywords is concerned with improving the quality of service provision of mobile networks from segmentation through network slicing by using deep reinforcement learning (DRL) algorithms for space allocation and division in the network, according to the consumption of resources or the needs of the users in the network [69].…”

Section: Resultsmentioning

confidence: 94%

Research Trends in the Use of Machine Learning Applied in Mobile Networks: A Bibliometric Approach and Research Agenda

García-Pineda,

Valencia-Arias,

Patiño-Vanegas

et al. 2023

Informatics

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This article aims to examine the research trends in the development of mobile networks from machine learning. The methodological approach starts from an analysis of 260 academic documents selected from the Scopus and Web of Science databases and is based on the parameters of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Quantity, quality and structure indicators are calculated in order to contextualize the documents’ thematic evolution. The results reveal that, in relation to the publications by country, the United States and China, who are competing for fifth generation (5G) network coverage and are responsible for manufacturing devices for mobile networks, stand out. Most of the research on the subject focuses on the optimization of resources and traffic to guarantee the best management and availability of a network due to the high demand for resources and greater amount of traffic generated by the many Internet of Things (IoT) devices that are being developed for the market. It is concluded that thematic trends focus on generating algorithms for recognizing and learning the data in the network and on trained models that draw from the available data to improve the experience of connecting to mobile networks.

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Section: Resultsmentioning

confidence: 94%

Research Trends in the Use of Machine Learning Applied in Mobile Networks: A Bibliometric Approach and Research Agenda

García-Pineda,

Valencia-Arias,

Patiño-Vanegas

et al. 2023

Informatics

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“…As a result, there is less interest in data sharing from both patients and hospital staff. The introduction of federated learning [220] , [221] , which eliminates the need for data transfers from the source, or the use of Blockchain-based security measures for secure and transparent data transfers from source to destination are two privacy measures that need to be focused on in order to deal with this issue [222] , [223] .…”

Section: And DL In Smart Healthcare: Challenges and Opportunitiesmentioning

confidence: 99%

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

Rahman,

Debnath,

Kundu

et al. 2024

AIMSPH

Self Cite

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<abstract> <p>In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our observations.</p> </abstract>

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“…Regulation efforts aim to safeguard user privacy, yet there are still gaps due to the decentralized nature of network management and the lack of standardized privacy protocols. Challenges include balancing privacy with network functionality, addressing jurisdictional laws in regulation, and mitigating vulnerabilities exploited by malicious actors [165]- [169]. Effective privacy measures demand collaboration among stakeholders to develop robust frameworks that uphold user rights while preserving network integrity.…”

Section: Privacy Concerns In the Network Interface Layermentioning

confidence: 99%

A comprehensive survey of performance, security and privacy issues in the network interface layer of the TCP/IP

Emmanuel Asituha

2024

GSC Adv. Res. Rev.

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The network interface layer of the TCP/IP protocol suite, primarily comprised of the Internet Protocol (IP), serves as the backbone of modern internet communication. With its efficient data delivery, The network interface layer, presents key challenges in terms of performance, security, and privacy. This comprehensive survey delves into these three crucial aspects, analyzing the inherent vulnerabilities, limitations of the interface layer, and provide solutions of the related problems. The performance analysis explores throughput, latency, and bandwidth constraints, along with solutions such as bandwidth allocation and optimization techniques. Vulnerabilities within Network Interface Layer, including denial-of-service attacks and MAC address spoofing, are discussed, along with a review of existing security mechanisms. Privacy flaws are examined, covering MAC address tracking, profiling risks, and anonymization techniques, while also addressing privacy considerations on the Internet of Things. The survey analyzes several case studies providing comparative analysis of the network interface layer protocols, with support of the real world scenarios including performance analysis in high density environment, and security and privacy risks in smart homes networks. The findings provide a comprehensive understanding of the complexities surrounding performance, security, and privacy issues future directions and potential solutions.

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On the ICN-IoT with federated learning integration of communication: Concepts, security-privacy issues, applications, and future perspectives

Cited by 59 publications

References 139 publications

Research Trends in the Use of Machine Learning Applied in Mobile Networks: A Bibliometric Approach and Research Agenda

Research Trends in the Use of Machine Learning Applied in Mobile Networks: A Bibliometric Approach and Research Agenda

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

A comprehensive survey of performance, security and privacy issues in the network interface layer of the TCP/IP

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