{"title":"开发安全指标,评估电网应对电动汽车充电生态系统攻击的态势","authors":"Ahmadreza Abazari;Mohsen Ghafouri;Danial Jafarigiv;Ribal Atallah;Chadi Assi","doi":"10.1109/TSG.2024.3451970","DOIUrl":null,"url":null,"abstract":"Providing reliable and efficient services for EV users necessitates the use of cyber layers on top of physical layers in EV ecosystems. The deployment of such cyber layers, however, makes these ecosystems an appealing target for various cyber-attacks—e.g., data manipulation, malware injection, and intrusions—which are crafted to deteriorate the operation of power distribution networks. On this basis, this paper develops a metric that captures the security posture of EV ecosystems, considering the possible attacks and their associated impacts on distribution grids. First, potential attack graphs are obtained to show the connections between the adversaries’ access points and the consequences of attack vectors. Then, a Markov decision process (MDP) tree is generated, using probabilities of adversaries’ success rates for a specific attack vector and unique reward functions. The developed MDP tree is then resolved by a policy iteration algorithm to calculate the value function of each state, related subsequent adversarial actions from the attackers’ viewpoint, and quantify the security posture of each state. Finally, using the obtained metric, a deep convolutional neural network (CNN) is trained offline to notify the distribution system operators (DSOs) of the security status of EV ecosystems, i.e., secure and alarm situations. DSOs can use the developed security metrics to design consequent corrective actions during critical cyber attacks. To demonstrate the usefulness of the proposed security metric in quantifying the security status of the grid, a cyber-physical testbed is built. This testbed integrates a virtual sphere (vSphere) to simulate the cyber parts of the EV ecosystem as well as a real-time simulator to model two distribution networks, i.e., IEEE 33- and 141-bus, under DSO control center based on IEC 61850. For a distribution network with dynamic sections that can be created using the operation of tie-switches, a supplementary strategy has also been suggested. This strategy is evaluated under the IEEE 69-bus distribution network to calculate the related security metric and update the security monitoring framework.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 1","pages":"254-276"},"PeriodicalIF":8.6000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Developing a Security Metric for Assessing the Power Grid’s Posture Against Attacks From EV Charging Ecosystem\",\"authors\":\"Ahmadreza Abazari;Mohsen Ghafouri;Danial Jafarigiv;Ribal Atallah;Chadi Assi\",\"doi\":\"10.1109/TSG.2024.3451970\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Providing reliable and efficient services for EV users necessitates the use of cyber layers on top of physical layers in EV ecosystems. The deployment of such cyber layers, however, makes these ecosystems an appealing target for various cyber-attacks—e.g., data manipulation, malware injection, and intrusions—which are crafted to deteriorate the operation of power distribution networks. On this basis, this paper develops a metric that captures the security posture of EV ecosystems, considering the possible attacks and their associated impacts on distribution grids. First, potential attack graphs are obtained to show the connections between the adversaries’ access points and the consequences of attack vectors. Then, a Markov decision process (MDP) tree is generated, using probabilities of adversaries’ success rates for a specific attack vector and unique reward functions. The developed MDP tree is then resolved by a policy iteration algorithm to calculate the value function of each state, related subsequent adversarial actions from the attackers’ viewpoint, and quantify the security posture of each state. Finally, using the obtained metric, a deep convolutional neural network (CNN) is trained offline to notify the distribution system operators (DSOs) of the security status of EV ecosystems, i.e., secure and alarm situations. DSOs can use the developed security metrics to design consequent corrective actions during critical cyber attacks. To demonstrate the usefulness of the proposed security metric in quantifying the security status of the grid, a cyber-physical testbed is built. This testbed integrates a virtual sphere (vSphere) to simulate the cyber parts of the EV ecosystem as well as a real-time simulator to model two distribution networks, i.e., IEEE 33- and 141-bus, under DSO control center based on IEC 61850. For a distribution network with dynamic sections that can be created using the operation of tie-switches, a supplementary strategy has also been suggested. This strategy is evaluated under the IEEE 69-bus distribution network to calculate the related security metric and update the security monitoring framework.\",\"PeriodicalId\":13331,\"journal\":{\"name\":\"IEEE Transactions on Smart Grid\",\"volume\":\"16 1\",\"pages\":\"254-276\"},\"PeriodicalIF\":8.6000,\"publicationDate\":\"2024-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Smart Grid\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10664647/\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Smart Grid","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10664647/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Developing a Security Metric for Assessing the Power Grid’s Posture Against Attacks From EV Charging Ecosystem
Providing reliable and efficient services for EV users necessitates the use of cyber layers on top of physical layers in EV ecosystems. The deployment of such cyber layers, however, makes these ecosystems an appealing target for various cyber-attacks—e.g., data manipulation, malware injection, and intrusions—which are crafted to deteriorate the operation of power distribution networks. On this basis, this paper develops a metric that captures the security posture of EV ecosystems, considering the possible attacks and their associated impacts on distribution grids. First, potential attack graphs are obtained to show the connections between the adversaries’ access points and the consequences of attack vectors. Then, a Markov decision process (MDP) tree is generated, using probabilities of adversaries’ success rates for a specific attack vector and unique reward functions. The developed MDP tree is then resolved by a policy iteration algorithm to calculate the value function of each state, related subsequent adversarial actions from the attackers’ viewpoint, and quantify the security posture of each state. Finally, using the obtained metric, a deep convolutional neural network (CNN) is trained offline to notify the distribution system operators (DSOs) of the security status of EV ecosystems, i.e., secure and alarm situations. DSOs can use the developed security metrics to design consequent corrective actions during critical cyber attacks. To demonstrate the usefulness of the proposed security metric in quantifying the security status of the grid, a cyber-physical testbed is built. This testbed integrates a virtual sphere (vSphere) to simulate the cyber parts of the EV ecosystem as well as a real-time simulator to model two distribution networks, i.e., IEEE 33- and 141-bus, under DSO control center based on IEC 61850. For a distribution network with dynamic sections that can be created using the operation of tie-switches, a supplementary strategy has also been suggested. This strategy is evaluated under the IEEE 69-bus distribution network to calculate the related security metric and update the security monitoring framework.
期刊介绍:
The IEEE Transactions on Smart Grid is a multidisciplinary journal that focuses on research and development in the field of smart grid technology. It covers various aspects of the smart grid, including energy networks, prosumers (consumers who also produce energy), electric transportation, distributed energy resources, and communications. The journal also addresses the integration of microgrids and active distribution networks with transmission systems. It publishes original research on smart grid theories and principles, including technologies and systems for demand response, Advance Metering Infrastructure, cyber-physical systems, multi-energy systems, transactive energy, data analytics, and electric vehicle integration. Additionally, the journal considers surveys of existing work on the smart grid that propose new perspectives on the history and future of intelligent and active grids.