{"id":"https://openalex.org/W4284899818","doi":"https://doi.org/10.1109/eit53891.2022.9813983","title":"Residual Convolutional Network for Detecting Attacks on Intrusion Detection Systems in Smart Grid","display_name":"Residual Convolutional Network for Detecting Attacks on Intrusion Detection Systems in Smart Grid","publication_year":2022,"publication_date":"2022-05-19","ids":{"openalex":"https://openalex.org/W4284899818","doi":"https://doi.org/10.1109/eit53891.2022.9813983"},"language":"en","primary_location":{"id":"doi:10.1109/eit53891.2022.9813983","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eit53891.2022.9813983","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Electro Information Technology (eIT)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019676675","display_name":"Tala Talaei Khoei","orcid":"https://orcid.org/0000-0002-7630-9034"},"institutions":[{"id":"https://openalex.org/I24571045","display_name":"University of North Dakota","ror":"https://ror.org/04a5szx83","country_code":"US","type":"education","lineage":["https://openalex.org/I24571045"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tala Talaei Khoei","raw_affiliation_strings":["University of North Dakota,School of Computer Science and Electrical Engineering,Grand Forks,ND,USA,58203"],"affiliations":[{"raw_affiliation_string":"University of North Dakota,School of Computer Science and Electrical Engineering,Grand Forks,ND,USA,58203","institution_ids":["https://openalex.org/I24571045"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023144248","display_name":"Hu Wen","orcid":"https://orcid.org/0000-0001-9016-0734"},"institutions":[{"id":"https://openalex.org/I24571045","display_name":"University of North Dakota","ror":"https://ror.org/04a5szx83","country_code":"US","type":"education","lineage":["https://openalex.org/I24571045"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wen Chen Hu","raw_affiliation_strings":["University of North Dakota,School of Computer Science and Electrical Engineering,Grand Forks,ND,USA,58203"],"affiliations":[{"raw_affiliation_string":"University of North Dakota,School of Computer Science and Electrical Engineering,Grand Forks,ND,USA,58203","institution_ids":["https://openalex.org/I24571045"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071211423","display_name":"Naima Kaabouch","orcid":null},"institutions":[{"id":"https://openalex.org/I24571045","display_name":"University of North Dakota","ror":"https://ror.org/04a5szx83","country_code":"US","type":"education","lineage":["https://openalex.org/I24571045"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Naima Kaabouch","raw_affiliation_strings":["University of North Dakota,School of Computer Science and Electrical Engineering,Grand Forks,ND,USA,58203"],"affiliations":[{"raw_affiliation_string":"University of North Dakota,School of Computer Science and Electrical Engineering,Grand Forks,ND,USA,58203","institution_ids":["https://openalex.org/I24571045"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5019676675"],"corresponding_institution_ids":["https://openalex.org/I24571045"],"apc_list":null,"apc_paid":null,"fwci":1.6561,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.84235895,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"231","last_page":"237"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.9997000098228455,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9987000226974487,"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":0.8455138206481934},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7548360824584961},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7445328235626221},{"id":"https://openalex.org/keywords/false-alarm","display_name":"False alarm","score":0.6399527788162231},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.6315903067588806},{"id":"https://openalex.org/keywords/constant-false-alarm-rate","display_name":"Constant false alarm rate","score":0.5657448768615723},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5205572247505188},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.5037612318992615},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4907868802547455},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47475865483283997},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4246430993080139},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41082829236984253},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3589407503604889},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.13060614466667175}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8455138206481934},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7548360824584961},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7445328235626221},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.6399527788162231},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.6315903067588806},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.5657448768615723},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5205572247505188},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.5037612318992615},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4907868802547455},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47475865483283997},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4246430993080139},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41082829236984253},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3589407503604889},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.13060614466667175},{"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/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/eit53891.2022.9813983","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eit53891.2022.9813983","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Electro Information Technology (eIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2296509296","https://openalex.org/W2334853001","https://openalex.org/W2343112621","https://openalex.org/W2804368608","https://openalex.org/W2890507837","https://openalex.org/W2911264084","https://openalex.org/W2928842143","https://openalex.org/W2955014922","https://openalex.org/W2964962196","https://openalex.org/W2972618277","https://openalex.org/W3006271479","https://openalex.org/W3011203842","https://openalex.org/W3035311645","https://openalex.org/W3043486047","https://openalex.org/W3087958975","https://openalex.org/W3089214982","https://openalex.org/W3091379954","https://openalex.org/W3102142968","https://openalex.org/W3164105561","https://openalex.org/W3186003301","https://openalex.org/W4205637629","https://openalex.org/W4206534607","https://openalex.org/W4214897310","https://openalex.org/W6766202003","https://openalex.org/W6807145087"],"related_works":["https://openalex.org/W2560215812","https://openalex.org/W2949601986","https://openalex.org/W4293226380","https://openalex.org/W2788972299","https://openalex.org/W1983393909","https://openalex.org/W2040150569","https://openalex.org/W2468095590","https://openalex.org/W2132174924","https://openalex.org/W1911540634","https://openalex.org/W2013909972"],"abstract_inverted_index":{"Smart":[0],"grid":[1],"provides":[2],"several":[3,16,39],"benefits,":[4,12],"such":[5,46],"as":[6],"reliability":[7],"and":[8,61,126,144],"affordability.":[9],"Despite":[10],"its":[11],"this":[13,37,99],"network":[14,94],"has":[15],"shortcomings,":[17],"including":[18],"a":[19,77,86,91,132,138,145],"lack":[20],"of":[21,28,51,58,112,130,134,136,140,142,147,150],"security.":[22],"DoS":[23],"attacks":[24],"are":[25,104],"considered":[26],"one":[27],"the":[29,49,64,101,110,113,119],"main":[30],"damaging":[31],"cyber-attacks":[32],"on":[33],"these":[34,52],"networks.":[35],"For":[36],"purpose,":[38],"techniques":[40,53],"have":[41,75],"been":[42],"proposed":[43,120],"to":[44,108],"detect":[45],"attacks.":[47],"However,":[48],"majority":[50],"deal":[54],"with":[55,95],"high":[56],"rates":[57],"false":[59,148],"alarm":[60,149],"misdetection.":[62],"In":[63,98],"last":[65],"few":[66],"years,":[67],"deep":[68],"learning":[69],"techniques,":[70],"particularly":[71],"convolutional":[72,87],"neural":[73,88,93],"networks,":[74],"received":[76],"great":[78],"interest":[79],"for":[80],"detecting":[81],"cyber-attacks.":[82],"This":[83],"study":[84],"proposes":[85],"network-based":[89],"technique,":[90,100],"residual":[92],"50":[96],"layers.":[97],"tabular":[102],"data":[103],"changed":[105],"into":[106],"images":[107],"improve":[109],"performance":[111],"model.":[114],"The":[115],"results":[116],"show":[117],"that":[118],"model":[121],"outperforms":[122],"other":[123],"ML":[124],"models":[125],"achieves":[127],"an":[128],"accuracy":[129],"98.1%,":[131],"probability":[133,139,146],"detection":[135],"99.12%,":[137],"misdetection":[141],"0.88%,":[143],"1.03%.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2}],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2025-10-10T00:00:00"}
