The EAST MDSplus Access Data Monitoring and Early Warning System Based on Elastic Stack

The exponential growth of experimental data generated by the Experimental Advanced Superconducting Tokamak (EAST) tokamak device necessitates enhanced monitoring of MDSplus data access patterns. While existing MDSplus infrastructure records basic connection metadata, critical gaps persist in auditing granular operational details (e.g., specific data operations, user attribution, and access patterns), creating vulnerabilities for large-scale fusion data security. These gaps hinder the ability to detect unauthorized activities, ensure compliance with data governance standards, and protect sensitive experimental data from misuse or breaches. To address MDSplus’s auditing limitations in fusion data security, we develop an access data monitoring and early warning system with three core contributions: A multidimensional logging architecture designed to track user-specific operations and access patterns, thereby obtaining comprehensive audit trails; an elastic stack-based infrastructure enabling real-time analysis and storage of Terabyte-scale audit trail; and dual monitoring interfaces integrating anomaly detection algorithms for both real-time alerts and offline investigations. Our framework establishes a paradigm for high-resolution diagnostics in fusion experiment data governance, which can be readily applied to other fusion experimental devices that use MDSplus for data storage, and provides significant value for monitoring fusion experiment data and ensuring data security.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Plasma Science
Published
2025-05-19
DOI
https://doi.org/10.1109/tps.2025.3564700
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The EAST MDSplus Access Data Monitoring and Early Warning System Based on Elastic Stack

F. Wang, Y. Zhang
IEEE Transactions on Plasma Science
Distributed and Parallel Computing Systems
article

The EAST MDSplus Access Data Monitoring and Early Warning System Based on Elastic Stack

F. Wang, Y. Zhang
article en

Abstract

The exponential growth of experimental data generated by the Experimental Advanced Superconducting Tokamak (EAST) tokamak device necessitates enhanced monitoring of MDSplus data access patterns. While existing MDSplus infrastructure records basic connection metadata, critical gaps persist in auditing granular operational details (e.g., specific data operations, user attribution, and access patterns), creating vulnerabilities for large-scale fusion data security. These gaps hinder the ability to detect unauthorized activities, ensure compliance with data governance standards, and protect sensitive experimental data from misuse or breaches. To address MDSplus’s auditing limitations in fusion data security, we develop an access data monitoring and early warning system with three core contributions: A multidimensional logging architecture designed to track user-specific operations and access patterns, thereby obtaining comprehensive audit trails; an elastic stack-based infrastructure enabling real-time analysis and storage of Terabyte-scale audit trail; and dual monitoring interfaces integrating anomaly detection algorithms for both real-time alerts and offline investigations. Our framework establishes a paradigm for high-resolution diagnostics in fusion experiment data governance, which can be readily applied to other fusion experimental devices that use MDSplus for data storage, and provides significant value for monitoring fusion experiment data and ensuring data security.

IEEE Transactions on Plasma ScienceVol. 53(6)
University of Science and Technology of China (CN), Chinese Academy of Sciences (CN), Hefei Institutes of Physical Science (CN), Institute of Plasma Physics (CN)
Climate action
Openalex Percentile: Top 89%
Distributed and Parallel Computing Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.