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Related Experiment Video

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Security of Separated Data in Cloud Systems with Competing Attack Detection and Data Theft Processes.

Gregory Levitin1,2, Liudong Xing3, Hong-Zhong Huang1

  • 1Center for System Reliability and Safety, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China.

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Summary

This study introduces a data partitioning strategy and early warning agents (EWAs) to combat co-residence attacks in cloud computing. The research models attack success probability to optimize data security and minimize user costs.

Keywords:
Cloud systemco-residence attackcostdata partitiondata securityearly warningvirtual machine

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Area of Science:

  • Cloud Computing Security
  • Cybersecurity
  • Information Assurance

Background:

  • Virtualization enables shared physical servers for multiple users, creating vulnerabilities.
  • Co-residence attacks exploit shared resources to steal data.
  • Data partitioning and early warning agents (EWAs) are proposed security measures.

Purpose of the Study:

  • To model and analyze the attack success probability in cloud systems.
  • To determine optimal data partitioning and protection policies.
  • To minimize user costs while ensuring data security against co-residence attacks.

Main Methods:

  • Probabilistic modeling of competing attack detection and data theft processes.
  • Analysis of attack success probability based on system parameters.
  • Optimization of data partitioning and protection strategies.

Main Results:

  • The study quantifies attack success probability considering EWAs and attacker behavior.
  • Optimal policies are derived to balance security and cost.
  • Parameter sensitivity analysis reveals key factors influencing security.

Conclusions:

  • The proposed model provides a framework for securing cloud data against co-residence attacks.
  • Effective data partitioning and timely detection are crucial for cloud security.
  • The findings guide the development of robust cloud security strategies.