{"id":"https://openalex.org/W2990877697","doi":"https://doi.org/10.1109/isgt45199.2020.9087649","title":"Attack on Grid Event Cause Analysis: An Adversarial Machine Learning Approach","display_name":"Attack on Grid Event Cause Analysis: An Adversarial Machine Learning Approach","publication_year":2020,"publication_date":"2020-02-01","ids":{"openalex":"https://openalex.org/W2990877697","doi":"https://doi.org/10.1109/isgt45199.2020.9087649","mag":"2990877697"},"language":"en","primary_location":{"id":"doi:10.1109/isgt45199.2020.9087649","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isgt45199.2020.9087649","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1911.08011","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022752230","display_name":"Iman Niazazari","orcid":null},"institutions":[{"id":"https://openalex.org/I134113660","display_name":"University of Nevada, Reno","ror":"https://ror.org/01keh0577","country_code":"US","type":"education","lineage":["https://openalex.org/I134113660"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Iman Niazazari","raw_affiliation_strings":["Department of Electrical and Biomedical Engineering, University of Nevada, Reno, NV, USA","University of Nevada,Department of Electrical and Biomedical Engineering,Reno,NV,USA,89557"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Biomedical Engineering, University of Nevada, Reno, NV, USA","institution_ids":["https://openalex.org/I134113660"]},{"raw_affiliation_string":"University of Nevada,Department of Electrical and Biomedical Engineering,Reno,NV,USA,89557","institution_ids":["https://openalex.org/I134113660"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072772883","display_name":"Hanif Livani","orcid":"https://orcid.org/0000-0002-7043-2728"},"institutions":[{"id":"https://openalex.org/I134113660","display_name":"University of Nevada, Reno","ror":"https://ror.org/01keh0577","country_code":"US","type":"education","lineage":["https://openalex.org/I134113660"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hanif Livani","raw_affiliation_strings":["Department of Electrical and Biomedical Engineering, University of Nevada, Reno, NV, USA","University of Nevada,Department of Electrical and Biomedical Engineering,Reno,NV,USA,89557"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Biomedical Engineering, University of Nevada, Reno, NV, USA","institution_ids":["https://openalex.org/I134113660"]},{"raw_affiliation_string":"University of Nevada,Department of Electrical and Biomedical Engineering,Reno,NV,USA,89557","institution_ids":["https://openalex.org/I134113660"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I134113660"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00855498,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10917","display_name":"Smart Grid Security and Resilience","score":0.9592999815940857,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9577999711036682,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7701228857040405},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7157459855079651},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6904475688934326},{"id":"https://openalex.org/keywords/vulnerability","display_name":"Vulnerability (computing)","score":0.5801992416381836},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5312901139259338},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.5303569436073303},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.48230648040771484},{"id":"https://openalex.org/keywords/adversarial-machine-learning","display_name":"Adversarial machine learning","score":0.4722425639629364},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.46089616417884827},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44422146677970886},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44001123309135437},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.40879756212234497},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35836952924728394},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.12642714381217957}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7701228857040405},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7157459855079651},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6904475688934326},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.5801992416381836},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5312901139259338},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.5303569436073303},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.48230648040771484},{"id":"https://openalex.org/C2778403875","wikidata":"https://www.wikidata.org/wiki/Q20312394","display_name":"Adversarial machine learning","level":3,"score":0.4722425639629364},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.46089616417884827},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44422146677970886},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44001123309135437},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.40879756212234497},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35836952924728394},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.12642714381217957},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/isgt45199.2020.9087649","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isgt45199.2020.9087649","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1911.08011","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1911.08011","pdf_url":"https://arxiv.org/pdf/1911.08011","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:osti.gov:1958805","is_oa":true,"landing_page_url":"https://www.osti.gov/biblio/1958805","pdf_url":"https://www.osti.gov/servlets/purl/1958805","source":{"id":"https://openalex.org/S4306402487","display_name":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I139351228","host_organization_name":"Office of Scientific and Technical Information","host_organization_lineage":["https://openalex.org/I139351228"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},{"id":"mag:2990877697","is_oa":true,"landing_page_url":"http://arxiv.org/pdf/1911.08011.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1911.08011","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1911.08011","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1911.08011","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1911.08011","pdf_url":"https://arxiv.org/pdf/1911.08011","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.7699999809265137}],"awards":[{"id":"https://openalex.org/G239385309","display_name":null,"funder_award_id":"DE-OE0000911","funder_id":"https://openalex.org/F4320333450","funder_display_name":"National Energy Technology Laboratory"},{"id":"https://openalex.org/G8076765164","display_name":null,"funder_award_id":"DE-OE0000911","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"},{"id":"https://openalex.org/G8457434953","display_name":null,"funder_award_id":"OE0000911","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"}],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320333450","display_name":"National Energy Technology Laboratory","ror":"https://ror.org/01x26mz03"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2095769030","https://openalex.org/W2180612164","https://openalex.org/W2243397390","https://openalex.org/W2603766943","https://openalex.org/W2604519205","https://openalex.org/W2739824434","https://openalex.org/W2782107121","https://openalex.org/W2787299888","https://openalex.org/W2800069536","https://openalex.org/W2921073389","https://openalex.org/W2963207607","https://openalex.org/W2963857521","https://openalex.org/W2964153729","https://openalex.org/W2964351981","https://openalex.org/W4235603051","https://openalex.org/W6736288562"],"related_works":["https://openalex.org/W3003753545","https://openalex.org/W2915576822","https://openalex.org/W2964070825","https://openalex.org/W2902351501","https://openalex.org/W3094947020","https://openalex.org/W3114686421","https://openalex.org/W3204654406","https://openalex.org/W3088451294","https://openalex.org/W3008618285","https://openalex.org/W3080416586","https://openalex.org/W2535751405","https://openalex.org/W3081342331","https://openalex.org/W3115987681","https://openalex.org/W3182741953","https://openalex.org/W2920763889","https://openalex.org/W2986549959","https://openalex.org/W565260256","https://openalex.org/W3013996776","https://openalex.org/W3038196750","https://openalex.org/W3116908162"],"abstract_inverted_index":{"With":[0],"the":[1,17,38,50,60,66,83,103,114,117,130,133,138],"ever-increasing":[2],"reliance":[3],"on":[4,71,128],"data":[5,20,27,39,44,93,139],"for":[6],"data-driven":[7],"applications":[8],"in":[9],"power":[10],"grids,":[11],"such":[12,40,149],"as":[13,150],"event":[14,56,75,118],"cause":[15,76,119],"analysis,":[16],"authenticity":[18],"of":[19,52,68,85,105,116,125,132,152,156],"streams":[21],"has":[22],"become":[23],"crucially":[24],"important.":[25],"The":[26,95,123],"can":[28],"be":[29],"prone":[30],"to":[31,36,87,102,112,158],"adversarial":[32,69,126],"stealthy":[33,92],"attacks":[34,70,127,153],"aiming":[35],"manipulate":[37],"that":[41],"residual-based":[42],"bad":[43],"detectors":[45],"cannot":[46],"detect":[47],"them,":[48],"and":[49,154],"perception":[51],"system":[53],"operators":[54],"or":[55],"classifiers":[57],"changes":[58],"about":[59],"actual":[61],"event.":[62],"This":[63],"paper":[64],"investigates":[65],"impact":[67],"convolutional":[72],"neural":[73],"network-based":[74],"analysis":[77,120],"frameworks.":[78],"We":[79],"have":[80],"successfully":[81],"verified":[82],"ability":[84],"adversaries":[86],"maliciously":[88],"misclassify":[89],"events":[90],"through":[91],"manipulations.":[94],"vulnerability":[96],"assessment":[97],"is":[98,121,135],"studied":[99,136],"with":[100],"respect":[101],"number":[104],"compromised":[106],"measurements.":[107],"Furthermore,":[108],"a":[109],"defense":[110],"mechanism":[111],"robustify":[113],"performance":[115],"proposed.":[122],"effectiveness":[124],"changing":[129],"output":[131],"framework":[134],"using":[137],"generated":[140],"by":[141],"real-time":[142],"digital":[143],"simulator":[144],"(RTDS)":[145],"under":[146],"different":[147],"scenarios":[148],"type":[151],"level":[155],"access":[157],"data.":[159]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}