[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-291084-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-291084-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","cybersecurity-assessment-of-smart-grid-exposure-using-a-machine-learning-based-approach","Cybersecurity Assessment of Smart Grid Exposure Using a Machine Learning Based Approach","","Power-system stability is increasingly threatened by cyber intrusions, including unauthorized access to confidential data, malware injection, and exploitation of weaknesses in inadequately patched software. This work develops machine-learning–enabled assessment capabilities for real-time evaluation of power-system disturbances. Using the Mississippi State University and Oak Ridge National Laboratory dataset, an XGB Classifier identifies and classifies attack events, natural events, and no-events. Results show strong performance across all metrics for the three sub-datasets.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/cybersecurity-assessment-of-smart-grid-exposure-using-a-machine-learning-based-approach/291084/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/cybersecurity-assessment-of-smart-grid-exposure-using-a-machine-learning-based-approach/291084.png","ImageObject",300,407,{"name":42,"@type":43},"Theodora","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",8,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What problem does the study address in smart grid cybersecurity?","Question",{"text":63,"@type":64},"The study addresses the growing risk of cyber disturbances to stable power-system operation, including unauthorized data access, malware injection, and exploitation of software vulnerabilities.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which machine learning approach and dataset are used?",{"text":68,"@type":64},"An XGB Classifier is trained and evaluated using the Mississippi State University and Oak Ridge National Laboratory dataset.",{"name":70,"@type":61,"acceptedAnswer":71},"How are events classified in the proposed assessment?",{"text":72,"@type":64},"The model diagnoses and assesses three types of power-system events: Attack Events, Natural Events, and No-Events.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},291084,1789643405,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":55,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":107,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":80,"read_time":143,"preview_page_count":4},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","Cybersecurity Assessment of Smart Grid Exposure Using a Machine Learning Based Approach  \nMofe O. Jeje  \nDepartment of Computer Science  \nSchool of Electrical Engineering and Computer Science  \nUniversity of North Dakota  \nGrand Folks, North Dakoka, United States  \n[mofe.jeje@ndus.edu](mofe.jeje@ndus.edu)  \narXiv :2501 . 14175v1 [ cs .LG] 24 Jan 2025  \nAbstract—Given that disturbances to the stable and normal operation of power systems have grown phenomenally, particularly in terms of unauthorized access to confidential and critical data, injection of malicious software, and exploitation of security vulnerabilities in a poorly patched software among others; then developing, as a countermeasure, an assessment solutions with machine learning capabilities to match up in realtime, with the growth and fast pace of these cyber attacks, isnot only critical to the security, reliability and safe operation of power system, but also germane to guaranteeing advanced monitoring and efficient threat detection. Using the Mississippi State University and Oak Ridge National Laboratory dataset, the study used an XGB Classifier modeling approach in machine learning to diagnose and assess power system disturbances, in terms of Attack Events, Natural Events and No-Events. As test results show, the model, in all the three sub-datasets, generally demonstrates good performance on all metrics, as it relates to accurately identifying and classifying all the three power system events.  \nIndex Terms—Machine Learning, Smart Power Grid, Sychrophasor Network, SCADA Network, Cybersecurity.  \nI. INTRODUCTION  \nToday’s electrical power grid which has become integral and an indispensable part of modern society, has been largely threatened by growing risks of extreme events from, not only the natural disasters, but also by malicious attacks from cyber adversaries [1], targeting several key power system operational functions, such as automatic generation control (AGC), state estimation (SE), and energy management systems among many others (EMS) .  \nWhile threats from extreme events, in terms of natural disasters have been largely caused by climate change being somewhat induced, in part by inefficient power grid operations along with other engineering activities around the world; the threat from malicious attacks which falls within the scope of this study, is occasioned, on the other hand, by integration and deployment of the ever-changing Information and Communication Technologies (ICT) into the operation of electric power grid, with the overall goal of intelligently monitoring, controlling and protecting the power system.  \nSmart grid, being an answer to myriads of issues that characterized traditional power transmission and distribution  \nsystems, in terms of addressing the intermittent nature of renewable energy, mis-match between generated electricity and demand, energy waste, huge revenue losses, consumer non-participation, and an inefficient routing and dispensation of electricity among others; has, in another stretch, evoked concern and attracted significant attention towards the security of power systems. Even though the introduction of ICT into the power systems makes the power grid networks to become smarter, it also substantially expands current cyber attack surface area, by exposing the power grid to vulnerabilities that increases the risk of malicious cyber attacks from adversaries who may alter the underlying physical systems and processes, that could potentially compromise a country’s national security.  \nWorse still, while the Supervisory Control and Data Acquisition (SCADA) network which has been traditionally used for monitoring, controlling, and protection of power systems has a recurrent problem of low measurements of refresh rate that limits or inhibits real-time assessment of a wide area network; the use of Synchrophasor networks as a replacement, even though has high-speed digital signal processors, with reporting rates sufficiently high to","cbCaibZ4Z8naGQdf","https://ap.wps.com/l/cbCaibZ4Z8naGQdf","pdf",944560,"English","# Abstract\n# I. Introduction\n# II. Review of Literature\n## Applicability of Machine Learning in Detection Techniques","[{\"question\":\"What problem does the study address in smart grid cybersecurity?\",\"answer\":\"The study addresses the growing risk of cyber disturbances to stable power-system operation, including unauthorized data access, malware injection, and exploitation of software vulnerabilities.\"},{\"question\":\"Which machine learning approach and dataset are used?\",\"answer\":\"An XGB Classifier is trained and evaluated using the Mississippi State University and Oak Ridge National Laboratory dataset.\"},{\"question\":\"How are events classified in the proposed assessment?\",\"answer\":\"The model diagnoses and assesses three types of power-system events: Attack Events, Natural Events, and No-Events.\"}]","Cybersecurity Assessment of Smart Grid Exposure Using a Machine Learning Based Approach | PDF",18]