Computer cyberspace security mechanism supported by cloud computing

ZeYuan Fu

PLOS ONE, 2022, vol. 17, issue 10, 1-17

Abstract: To improve the cybersecurity of Cloud Computing (CC) system. This paper proposes a Network Anomaly Detection (NAD) model based on the Fuzzy-C-Means (FCM) clustering algorithm. Secondly, the Cybersecurity Assessment Model (CAM) based on Grey Relational Grade (GRG) is creatively constructed. Finally, combined with Rivest Shamir Adleman (RSA) algorithm, this work proposes a CC network-oriented data encryption technology, selects different data sets for different models, and tests each model through design experiments. The results show that the average Correct Detection Rate (CDR) of the NAD model for different types of abnormal data is 93.33%. The average False Positive Rate (FPR) and the average Unreported Rate (UR) are 6.65% and 16.27%, respectively. Thus, the NAD model can ensure a high detection accuracy in the case of sufficient data. Meanwhile, the cybersecurity situation prediction by the CAM is in good agreement with the actual situation. The error between the average value of cybersecurity situation prediction and the actual value is only 0.82%, and the prediction accuracy is high. The RSA algorithm can control the average encryption time for very large text, about 12s. The decryption time is slightly longer but within a reasonable range. For different-size text, the encryption time is maintained within 0.5s. This work aims to provide important technical support for anomaly detection, overall security situation analysis, and data transmission security protection of CC systems to improve their cybersecurity.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0271546

DOI: 10.1371/journal.pone.0271546

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