The open and distributed connection of the powersystem makes it vulnerable to various potential cyber-attacks,which may lead to power outages and even casualties. Therefore,the construction of attack and defense drill...The open and distributed connection of the powersystem makes it vulnerable to various potential cyber-attacks,which may lead to power outages and even casualties. Therefore,the construction of attack and defense drill (ADD) platforms forattack mechanism investigation and protection strategy evaluationhas become a research hotspot. However, for the massiveand heterogeneous security analysis data generated during thedrill, it is rare to have a comprehensive and intuitive methodto visually and efficiently display the perspective of the attackerand defender. In order to solve this problem, this paper proposesa visual analysis scheme of an ADD framework for a grid cyberphysicalsystem (GCPS) based on the interactive visual analysismethod. Specifically, it realizes system weakness discovery basedon knowledge visualization, optimization of the detection modeland visualization interaction. Finally, the case study on thesimulation platform of ADD proves the effectiveness of theproposed method.展开更多
Data imputation is an essential pre-processing task for data governance,aimed at filling in incomplete data.However,conventional data imputation methods can only partly alleviate data incompleteness using isolated tab...Data imputation is an essential pre-processing task for data governance,aimed at filling in incomplete data.However,conventional data imputation methods can only partly alleviate data incompleteness using isolated tabular data,and they fail to achieve the best balance between accuracy and eficiency.In this paper,we present a novel visual analysis approach for data imputation.We develop a multi-party tabular data association strategy that uses intelligent algorithms to identify similar columns and establish column correlations across multiple tables.Then,we perform the initial imputation of incomplete data using correlated data entries from other tables.Additionally,we develop a visual analysis system to refine data imputation candidates.Our interactive system combines the multi-party data imputation approach with expert knowledge,allowing for a better understanding of the relational structure of the data.This significantly enhances the accuracy and eficiency of data imputation,thereby enhancing the quality of data governance and the intrinsic value of data assets.Experimental validation and user surveys demonstrate that this method supports users in verifying and judging the associated columns and similar rows using theirdomain knowledge.展开更多
基金the Science and Technology Project of State Grid Corporation of China(Research on key technologies of integrated electric power network security simulation and verification environment,521304190004).
文摘The open and distributed connection of the powersystem makes it vulnerable to various potential cyber-attacks,which may lead to power outages and even casualties. Therefore,the construction of attack and defense drill (ADD) platforms forattack mechanism investigation and protection strategy evaluationhas become a research hotspot. However, for the massiveand heterogeneous security analysis data generated during thedrill, it is rare to have a comprehensive and intuitive methodto visually and efficiently display the perspective of the attackerand defender. In order to solve this problem, this paper proposesa visual analysis scheme of an ADD framework for a grid cyberphysicalsystem (GCPS) based on the interactive visual analysismethod. Specifically, it realizes system weakness discovery basedon knowledge visualization, optimization of the detection modeland visualization interaction. Finally, the case study on thesimulation platform of ADD proves the effectiveness of theproposed method.
基金Project supported by the Key R&D"Pioneer"Tackling Plan Program of Zhejiang Province,China(No.2023C01119)the"Ten Thousand Talents Plan"Science and Technology Innovation Leading Talent Program of Zhejiang Province,China(No.2022R52044)+1 种基金the Major Standardization Pilot Projects for the Digital Economy(Digital Trade Sector)of Zhejiang Province,China(No.SJ-Bz/2023053)the National Natural Science Foundationof China(No.62132017)。
文摘Data imputation is an essential pre-processing task for data governance,aimed at filling in incomplete data.However,conventional data imputation methods can only partly alleviate data incompleteness using isolated tabular data,and they fail to achieve the best balance between accuracy and eficiency.In this paper,we present a novel visual analysis approach for data imputation.We develop a multi-party tabular data association strategy that uses intelligent algorithms to identify similar columns and establish column correlations across multiple tables.Then,we perform the initial imputation of incomplete data using correlated data entries from other tables.Additionally,we develop a visual analysis system to refine data imputation candidates.Our interactive system combines the multi-party data imputation approach with expert knowledge,allowing for a better understanding of the relational structure of the data.This significantly enhances the accuracy and eficiency of data imputation,thereby enhancing the quality of data governance and the intrinsic value of data assets.Experimental validation and user surveys demonstrate that this method supports users in verifying and judging the associated columns and similar rows using theirdomain knowledge.
