Sina Gharebaghi, S. H. Hosseini, M. Izadi, A. Safdarian
{"title":"Impacts of bad data injection on power systems security: Intruder point of view","authors":"Sina Gharebaghi, S. H. Hosseini, M. Izadi, A. Safdarian","doi":"10.1109/SGC.2017.8308860","DOIUrl":null,"url":null,"abstract":"Nowadays communication and monitoring systems play an important role in power systems security. However, these systems are exposed to bad data injection through attackers. This paper is aimed to propose a priority order list of attacked buses in order to be fully informed about the attacker decision. To do so, system security is evaluated through two well-known risk indices including under voltage risk index and overloading risk index. A genetic algorithm (GA) is employed to determine the most severe attack for each of the security indices. The effectiveness and accuracy of the proposed model are scrutinized through simulations on the IEEE 30-bus network. The paper reveals that knowing the most important buses from the attacker point of view will considerably reduce the freedom degree of the attacker.","PeriodicalId":346749,"journal":{"name":"2017 Smart Grid Conference (SGC)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Smart Grid Conference (SGC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SGC.2017.8308860","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Nowadays communication and monitoring systems play an important role in power systems security. However, these systems are exposed to bad data injection through attackers. This paper is aimed to propose a priority order list of attacked buses in order to be fully informed about the attacker decision. To do so, system security is evaluated through two well-known risk indices including under voltage risk index and overloading risk index. A genetic algorithm (GA) is employed to determine the most severe attack for each of the security indices. The effectiveness and accuracy of the proposed model are scrutinized through simulations on the IEEE 30-bus network. The paper reveals that knowing the most important buses from the attacker point of view will considerably reduce the freedom degree of the attacker.