A Security Model of Ubiquitous Power Internet of Things Based on SDN and DFI

Wang, Yan; Wei, Xiaogang

doi:10.1109/ictc49638.2020.9123271

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…In order to accurately identify different network services on dynamic ports, some researchers have proposed network traffic identification methods based on payload characteristics, such as deep packet inspection (DPI) [12,13] and deep flow inspection (DFI) [14,15]. e DPI technology mainly determines the type of each network service by judging whether the network characteristics match the fingerprint library characteristics.…”

Section: Related Workmentioning

confidence: 99%

“…is method believes that different applications will present different states on network session connections or data streams, but DFI does not pay attention to application load. In order to achieve the purpose of lightweight protection of the power Internet of ings, Wang and Wei [14] used DFI technology to analyze the collected network traffic of the Internet of ings and formed a feasible security prevention and control strategy model.…”

Section: Related Workmentioning

confidence: 99%

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Classification of Abnormal Traffic in Smart Grids Based on GACNN and Data Statistical Analysis

Zhang

et al. 2021

Security and Communication Networks

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With the continuous development of smart grids, communication networks carry more and more power services, and at the same time, they are also facing more and more security issues. For example, some malicious software usually uses encryption technology or tunnel technology to bypass firewalls, intrusion detection systems, etc., thereby posing a serious threat to the information security of smart grids. At present, the classification of network traffic mainly depends on the correct extraction of network protocol characteristics. However, the process of extracting network features by some traditional methods is time-consuming and overly dependent on experience. In order to solve the problem of accurate classification of power network traffic, this paper proposes a method of convolutional neural network based on genetic algorithm optimization (GACNN) and data statistical analysis. This method can simultaneously extract the time characteristics between different packet groups and the spatial characteristics in the same packet group. Therefore, it greatly saves manpower and gets rid of the dependence on experience value. The proposed method has been tested and verified on the UNSW-NB15 dataset and the real dataset collected by the power company. The results show that the proposed method can correctly classify abnormal network flows and is much better than traditional machine learning methods. In large-scale real network flow scenarios, the detection rate of the proposed method exceeds 97%, while the traditional method is generally less than 90%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Classification of Abnormal Traffic in Smart Grids Based on GACNN and Data Statistical Analysis

Zhang

et al. 2021

Security and Communication Networks

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“…The platform offers a pliant and expandable infrastructure for the storage and retrieval of educational materials (Wannapiroon et al, 2019). The emergence of the Internet of things (IoT) (Davies et al, 2020) is beginning to influence the development of Ubiquitous Learning environments (Wang et al, 2020). IoT facilitates the interconnection of commonplace objects in a digital manner (Kaur & Kaur, 2017), thereby enabling the generation of data that can be utilized to enrich and augment the learning process (Weeber et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A cross-database bibliometric analysis of ubiquitous learning: Trends, influences, and future directions

Abayeva,

Orazayeva,

Omirbek

et al. 2023

CONT ED TECHNOLOGY

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The concept of ubiquitous learning has emerged as a pedagogical approach in response to the advancements made in mobile, wireless communication, and sensing technologies. The domain of ubiquitous learning is distinguished by swift progression, thereby presenting a difficulty in maintaining current knowledge of its developments. The implementation of bibliometric analysis would enable the tracking of its development and current status. The objective of the present investigation is to perform a thorough bibliometric examination of the domain of ubiquitous learning. This research aims to discern significant attributes, patterns, and influencers within the discipline by analyzing scholarly works. The primary objective of this study is to provide a comprehensive depiction of the salient characteristics and patterns exhibited by the datasets employed in ubiquitous learning research, namely Scopus, Web of Science (WoS), and merged datasets. Additionally, the study seeks to trace the historical development of publications in this domain and to ascertain the most noteworthy publications and authors that have exerted a significant impact on this field. This study provides an extensive bibliometric analysis of ubiquitous learning, examining output from Scopus, WoS, and a merged dataset. It highlights the field’s growth and the rising use of diverse data sources, with Scopus and the merged dataset revealing broader insights. The analysis reveals an interest peak in 2016 and a subsequent decline likely due to incomplete recent data. Documents, predominantly articles, differ across databases, underscoring the unique contributions of each. The study identifies “Lecture Notes in Computer Science” and “Ubiquitous Learning” as major research sources. It recognizes Hwang, G.-J. as a highly influential author, with Asian institutions leading in research output. However, Western institutions also show strong representation in WoS and merged databases. Despite variations in total citation counts, countries like China, Switzerland, and Ireland contribute significantly to the field. Terms like “mobile learning” and “life log” have vital roles in bridging research clusters, while thematic maps reveal evolving trends like mobile learning and learning analytics. The collaborative structure and key figures in ubiquitous learning are illuminated through network analysis, emphasizing the importance of cross-database analysis for a comprehensive view of the field.

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Analysis of Various Security Schemes of Internet of Things

Kapoor

Kaur

2022

Information and Communication Technology for Competitive Strategies (ICTCS 2021)

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A Security Model of Ubiquitous Power Internet of Things Based on SDN and DFI

Cited by 4 publications

References 5 publications

Classification of Abnormal Traffic in Smart Grids Based on GACNN and Data Statistical Analysis

Classification of Abnormal Traffic in Smart Grids Based on GACNN and Data Statistical Analysis

A cross-database bibliometric analysis of ubiquitous learning: Trends, influences, and future directions

Analysis of Various Security Schemes of Internet of Things

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