Research on prediction of school computer network security situation based on IOT

Wei Yan (), Lei Qiao (), Singamaneni Krishnapriya () and Rahul Neware ()
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Wei Yan: Jiangsu Ocean University, Information Management
Lei Qiao: Jiangsu Ocean University, Information Management
Singamaneni Krishnapriya: Acharya Nagarjuna University
Rahul Neware: Høgskulen På Vestlandet

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 1, No 50, 488-495

Abstract: Abstract The degree of network attack methods is limitless, and the conventional defence and detection mode cannot satisfy the needs of network security monitoring, thanks to the fast growth of network technology and the large-scale expansion of networks. Widely distributed network system, the network system structure becomes increasingly complex, the network is facing increasingly attacks and threats, network security problems are increasingly serious, a variety of security incidents and vulnerabilities are increasingly frequent, including hacker attacks, and network security vulnerability is gradually exposed. Traditional ways of dealing with the problem may be insufficient, resulting in harm to persons and businesses, as well as the loss of property and a reduction in people's faith in the Internet. An IOT (Internet of Things) based orientated model is suggested to increase the accuracy in predicting network security condition for schools. The data is first rebuilt into a multidimensional time series, then fed into the support vector machine for training, and then nonlinear method is utilised to handle the problem of parameter optimization of the training mode to create the network security scenario prediction model. Finally, utilising IOT, the network security scenario prediction model is utilised to forecast network security in the future. The simulation results show that the optimal parameters of the support vector machine (SVM) are C = 10, σ = 0.625, and ε = 0.001. C = 10, σ = 0.625, ε = 0.001 were used to relearn the training set, the optimal network security situation prediction model was established, and the test set was predicted. The projected values were close to the actual values, and the accuracy of the forecast was high. The model, when paired with the nonlinear algorithm and SVM, can accurately reflect the network's overall security operation, enhance network security scenario forecast accuracy, and help the administrator in network security control.

Keywords: Network security; Network security situation prediction; Support vector machine (SVM); Prediction method (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13198-021-01479-z

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