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Related Experiment Video

Updated: Aug 26, 2025

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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Computer cyberspace security mechanism supported by cloud computing.

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  • 1School of Cyber Science and Engineering, Wuhan University, Wuhan, China.

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|October 7, 2022
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Summary
This summary is machine-generated.

This study enhances cloud computing cybersecurity using a Network Anomaly Detection (NAD) model with Fuzzy-C-Means (FCM) clustering, achieving 93.33% accuracy. It also introduces a Cybersecurity Assessment Model (CAM) and RSA encryption for robust system protection.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Data Science

Background:

  • Cloud Computing (CC) systems face significant cybersecurity challenges.
  • Effective anomaly detection and security assessment are crucial for CC environments.
  • Existing security measures require enhancement for comprehensive protection.

Purpose of the Study:

  • To propose an integrated approach for improving Cloud Computing cybersecurity.
  • To develop a Network Anomaly Detection (NAD) model for identifying threats.
  • To construct a Cybersecurity Assessment Model (CAM) for situation prediction and implement data encryption.

Main Methods:

  • Utilized the Fuzzy-C-Means (FCM) clustering algorithm for the NAD model.
  • Developed a Cybersecurity Assessment Model (CAM) based on Grey Relational Grade (GRG).
  • Integrated the Rivest Shamir Adleman (RSA) algorithm for data encryption in CC networks.

Main Results:

  • The NAD model achieved an average Correct Detection Rate (CDR) of 93.33% with low False Positive Rate (FPR) and Unreported Rate (UR).
  • The CAM demonstrated high prediction accuracy, with an average error of only 0.82% compared to actual cybersecurity situations.
  • RSA algorithm provided efficient encryption (approx. 12s for large text) and decryption within reasonable timeframes.

Conclusions:

  • The proposed NAD model offers high detection accuracy for anomalies in CC systems with sufficient data.
  • The CAM effectively predicts cybersecurity situations, providing valuable insights for system security.
  • The integrated approach enhances overall CC cybersecurity through improved anomaly detection, situation analysis, and secure data transmission.