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Research on fault-tolerant decision algorithm for data security automation
Jianxin Li1, Ruchun Jia2, Ning Xiang3
1China School of Cyberspace Security, Changzhou College of Information Technology, Changzhou, Jiangsu, China.
Frontiers in Big Data
|November 5, 2025
Summary
This study introduces an automated operation and maintenance decision algorithm incorporating data source security analysis. The new method enhances decision rationality and algorithm convergence while avoiding local optima.
Area of Science:
- Computer Science
- Artificial Intelligence
- Optimization Algorithms
Background:
- Traditional operation and maintenance algorithms lack data source security analysis, leading to susceptibility to noise, inefficiency, and irrational decisions.
- Existing methods often fail to address the critical aspect of data integrity in decision-making processes.
Purpose of the Study:
- To design an automated operation and maintenance decision algorithm that integrates data source security analysis.
- To improve the efficiency, rationality, and robustness of operation and maintenance decision-making.
Main Methods:
- Developed a multi-angle learning algorithm to model noise data and assess data source security.
- Constructed a particle swarm optimization (PSO) model with a niche mechanism to avoid local optima.
- Incorporated fault-tolerant analysis for data source security to eliminate bad and malicious data.
Main Results:
- The proposed algorithm significantly shortens operation and maintenance time.
- Enhanced decision-making rationality and improved algorithm convergence rates.
- Successfully avoided local optima and demonstrated improved performance through fault-tolerant analysis.
Conclusions:
- The integration of data source security analysis into automated decision algorithms is crucial for efficient and rational operation and maintenance.
- The developed algorithm offers a robust solution for optimizing decision-making in complex systems.
- Fault tolerance in data security further bolsters the reliability and convergence of the optimization process.
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