{"title":"A Support Vector Machines Security Assessment Method Based on Group Decision-Marking for Electric Power Information System","authors":"Xiaorong Cheng, Yan Wei, Xin Geng","doi":"10.1109/IAS.2009.234","DOIUrl":null,"url":null,"abstract":"In accordance with the characteristics and the special demands of electric power information system, this paper designs a support vector machines (SVM) risk assessment method based on group decision-marking. According to security technology indices of electric power information system, the method chooses the mode of expert scoring, based on the group decision-marking, to calculate integrated value of each index, which is as a training sample used to train SVM, and it forecasts risk level for the system. Finally, it verifies the correctness of the method by analyzing results of the examples of the electric power information system security assessment. The experiment shows that the method can not only forecast the current risk level of the electric power system with a high accuracy rate, but also reduce the influence of the subjective factors in some degree.","PeriodicalId":240354,"journal":{"name":"2009 Fifth International Conference on Information Assurance and Security","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Fifth International Conference on Information Assurance and Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAS.2009.234","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
In accordance with the characteristics and the special demands of electric power information system, this paper designs a support vector machines (SVM) risk assessment method based on group decision-marking. According to security technology indices of electric power information system, the method chooses the mode of expert scoring, based on the group decision-marking, to calculate integrated value of each index, which is as a training sample used to train SVM, and it forecasts risk level for the system. Finally, it verifies the correctness of the method by analyzing results of the examples of the electric power information system security assessment. The experiment shows that the method can not only forecast the current risk level of the electric power system with a high accuracy rate, but also reduce the influence of the subjective factors in some degree.
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