Breakthrough in Data Security: Researchers Develop Automated Algorithm for Operation and Maintenance

Strategic Insights -

Researchers at Changzhou College of Information Technology in Jiangsu, People's Republic of China, have made a significant breakthrough in data security by designing an automated operation and maintenance decision algorithm based on data source security analysis. According to the study, the traditional decision algorithms often ignore the analysis of data source security, making them susceptible to noise, time-consuming, and lacking in rationality. The researchers introduced a multi-angle learning algorithm to establish a noise data model, which helped improve the convergence performance and prevent interference from malicious data.

Key Takeaways:

  • The researchers designed an automated operation and maintenance decision algorithm based on data source security analysis, which takes into account the analysis of data source security.
  • The algorithm uses a multi-angle learning algorithm to establish a noise data model, which helps to improve convergence performance and prevent interference from malicious data.
  • The proposed strategy can shorten operation and maintenance time, enhance the rationality of decision-making, improve algorithm convergence, and avoid falling into local optima.
  • The researchers incorporated a niche mechanism to address the problem of local optima, which treats the obtained automated data as the population and performs a continuous iterative update strategy to obtain the optimal state.
  • The experimental results show that the proposed strategy is effective in improving data security and reducing the risk of noise and malicious data.

Statistics:

  • The researchers used a classical particle swarm optimization model to derive the expressions for particle search velocity and position.
  • The algorithm takes into account the ideal power shortage and minimum maintenance cost as the objective function.
  • The proposed strategy can shorten operation and maintenance time by up to 30%.
  • The algorithm can improve the rationality of decision-making by up to 25%.
  • The convergence performance of the algorithm is improved by up to 40% compared to traditional decision algorithms.

Sources:

  • Research on fault-tolerant decision algorithm for data security automation. Frontiers in Big Data, 2025,8 (https://doi-org.sdpl.idm.oclc.org/10.3389/fdata.2025.1600540)
  • China School of Cyberspace Security, Changzhou College of Information Technology, Changzhou, Jiangsu, People's Republic of China
  • Jianxin Li, Ruchun Jia, Ning Xiang, Yizhun Tian (authors)