Application of Virtual Private Network Technology in University Network Information Security

Yin, Chengrui

doi:10.1088/1742-6596/1915/4/042071

Cited by 3 publications

(4 citation statements)

References 4 publications

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“…Their rigorous research focuses on comparing the following three traffic generators, constant bit rate (CBR), file transfer protocol (FTP) and hypertext transfer protocol by varying their application in VPNs and non-VPNs. Throughput and time delay calculations allow for the deeper examination of performance [27]. Their results suggest a constant throughput in the case of CBR and a decrease in the case of FTP and HTTP for both situations and observed time delay increase as a consequence of changing the traffic generators.…”

Section: Benefits and Challenges Of Virtual Private Networkmentioning

confidence: 99%

Virtual Private Networks: Fundamentals, security issues and solutions

Mahmood¹

2023

Preprint

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The recent COVID-19 pandemic has showcased the implications of using virtual private networks (VPNs) as home-working is now common. Establishing the current state of knowledge on VPNs and their processes is vital. This corroborates an up to date understanding on the fundamentals of VPNs and their usage in today’s society; it informs on how to better use VPNs too. Insight into the security issues VPNs face and possible solutions to these allows for the identification of gaps for potential future research. Addressing these gaps would then indicate how to further improve VPNs, making sure VPNs are indeed beneficial to users.

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Section: Benefits and Challenges Of Virtual Private Networkmentioning

confidence: 99%

Virtual Private Networks: Fundamentals, security issues and solutions

Mahmood¹

2023

Preprint

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“…Due to the multi-dimensional problem of obtaining the features of the university's network data in Section II (B) (1), in order to improve the accuracy of threat detection in the university's network space security, a non-negative matrix decomposition algorithm is used to perform multi-feature dimensionality reduction on university network data [16]. The non-negative matrix decomposition algorithm has the advantages of simple decomposition implementation and small space occupation.…”

Section: ) Multi-feature Dimensionality Reduction Processing Of Unive...mentioning

confidence: 99%

“…The rapid development of the Internet has led to a large number of intrusion behaviours, posing a huge threat to the network security of universities. Therefore, achieving a comprehensive perception and defense of the university's cyberspace attack events is significant for building a complete network security defense system [1]- [3].…”

Section: Introductionmentioning

confidence: 99%

Construction of a Security Defense Model for the University's Cyberspace Based on Machine Learning

Bin

2023

IJACSA

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In order to ensure the security of university teachers and students using cyberspace, a machine learning based university cyberspace security defense model is constructed. Adopting a compression perception based data collection method for university cyberspace, the data information collection of university cyberspace is completed through sparse representation, compression measurement, and recovery reconstruction. Combining the advantages of Convolutional Neural Network (CNN) model in spatial feature extraction of data and Long Short Term Memory (LSTM) model in sequential feature extraction of data, extract the features of university network spatial data. After completing the multi feature dimensionality reduction processing of university network data based on the non-negative matrix decomposition algorithm, the feature dimensionality reduction processing results are input into the ConvLSTM-CNN model. After convolution calculation and integration, the security threat detection results of university network space are output. Based on the results of security threat detection, corresponding network attack defense measures are selected to ensure the security of the university's cyberspace. The experimental results show that the average attack interception rate of the model after application can reach 97.6%. It has been proven that building a model can accurately detect security threats to the university's cyberspace and achieve defense against various network attacks in different environments.

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“…Each data block contains the data of the last block, the current transaction, and transaction time. Therefore, for a new block, if the function value of the last block is verified equal to the value of all accounts before verification, the BC formed in this way is relatively easy to verify (Yin, 2021;Zheng et al, 2021b).…”

Section: Research On Nis and Fr Under Bc And Ecmentioning

confidence: 99%

Analysis of Safe Storage of Network Information Data and Financial Risks Under Blockchain Combined With Edge Computing

Liang¹,

Ruan

et al. 2022

Journal of Global Information Management

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To discuss the control of financial risks (FRs) under blockchain (BC) and improve network information security (NIS) and data security, edge computing (EC) combined with BC is proposed to control the risks of the big data (BD) financial system. Firstly, the BC-based financial system is introduced, and the characteristics of BC such as decentralization, tamper-resistant, and smart contract are analyzed. Secondly, the development status of NIS and the characteristics of marginal computing are explained, and the control model of NIS is established. Then, EC is used to encrypt the identity authentication system to ensure data security, and the BC-based FR evaluation model is established. Finally, a questionnaire is designed regarding the NIS model, and the results are analyzed. A simulation experiment is conducted regarding the index evaluation of the BC-based FR evaluation model. The experimental results indicate that network personnel control, environment, and technology have positive effects on NIS, and the impact factors are 0.26, 0.24, and 0.33, respectively.

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Application of Virtual Private Network Technology in University Network Information Security

Cited by 3 publications

References 4 publications

Virtual Private Networks: Fundamentals, security issues and solutions

Virtual Private Networks: Fundamentals, security issues and solutions

Construction of a Security Defense Model for the University's Cyberspace Based on Machine Learning

Analysis of Safe Storage of Network Information Data and Financial Risks Under Blockchain Combined With Edge Computing

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