{"id":"https://openalex.org/W4214608799","doi":"https://doi.org/10.1109/isgtasia49270.2021.9715557","title":"Time Delay Attack Detection Using Recurrent Variational Autoencoder and K-means Clustering","display_name":"Time Delay Attack Detection Using Recurrent Variational Autoencoder and K-means Clustering","publication_year":2021,"publication_date":"2021-12-05","ids":{"openalex":"https://openalex.org/W4214608799","doi":"https://doi.org/10.1109/isgtasia49270.2021.9715557"},"language":"en","primary_location":{"id":"doi:10.1109/isgtasia49270.2021.9715557","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isgtasia49270.2021.9715557","pdf_url":null,"source":{"id":"https://openalex.org/S4363605822","display_name":"2021 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia)","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/A5032851268","display_name":"Shahram Ghahremani","orcid":"https://orcid.org/0000-0001-9905-7530"},"institutions":[{"id":"https://openalex.org/I152815399","display_name":"Singapore University of Technology and Design","ror":"https://ror.org/05j6fvn87","country_code":"SG","type":"education","lineage":["https://openalex.org/I152815399"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Shahram Ghahremani","raw_affiliation_strings":["Singapore University of Technology and Design"],"affiliations":[{"raw_affiliation_string":"Singapore University of Technology and Design","institution_ids":["https://openalex.org/I152815399"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063936654","display_name":"David K. Y. Yau","orcid":"https://orcid.org/0000-0001-9061-7423"},"institutions":[{"id":"https://openalex.org/I152815399","display_name":"Singapore University of Technology and Design","ror":"https://ror.org/05j6fvn87","country_code":"SG","type":"education","lineage":["https://openalex.org/I152815399"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"David K.Y. Yau","raw_affiliation_strings":["Singapore University of Technology and Design"],"affiliations":[{"raw_affiliation_string":"Singapore University of Technology and Design","institution_ids":["https://openalex.org/I152815399"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087008513","display_name":"Justin Albrethsen","orcid":null},"institutions":[{"id":"https://openalex.org/I152815399","display_name":"Singapore University of Technology and Design","ror":"https://ror.org/05j6fvn87","country_code":"SG","type":"education","lineage":["https://openalex.org/I152815399"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Justin Albrethsen","raw_affiliation_strings":["Singapore University of Technology and Design"],"affiliations":[{"raw_affiliation_string":"Singapore University of Technology and Design","institution_ids":["https://openalex.org/I152815399"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110845794","display_name":"Rajvir Sidhu","orcid":null},"institutions":[{"id":"https://openalex.org/I152815399","display_name":"Singapore University of Technology and Design","ror":"https://ror.org/05j6fvn87","country_code":"SG","type":"education","lineage":["https://openalex.org/I152815399"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Rajvir Sidhu","raw_affiliation_strings":["Singapore University of Technology and Design"],"affiliations":[{"raw_affiliation_string":"Singapore University of Technology and Design","institution_ids":["https://openalex.org/I152815399"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057453537","display_name":"Ngai\u2010Man Cheung","orcid":"https://orcid.org/0000-0003-0135-3791"},"institutions":[{"id":"https://openalex.org/I152815399","display_name":"Singapore University of Technology and Design","ror":"https://ror.org/05j6fvn87","country_code":"SG","type":"education","lineage":["https://openalex.org/I152815399"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Ngai-Man Cheung","raw_affiliation_strings":["Singapore University of Technology and Design"],"affiliations":[{"raw_affiliation_string":"Singapore University of Technology and Design","institution_ids":["https://openalex.org/I152815399"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5032851268"],"corresponding_institution_ids":["https://openalex.org/I152815399"],"apc_list":null,"apc_paid":null,"fwci":0.616,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.60512565,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9998000264167786,"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":0.9998000264167786,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9958000183105469,"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/autoencoder","display_name":"Autoencoder","score":0.8862931132316589},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7873112559318542},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7634937763214111},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7258942127227783},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5546193718910217},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.531977117061615},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46894311904907227},{"id":"https://openalex.org/keywords/network-packet","display_name":"Network packet","score":0.4651331305503845},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4608108699321747},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4333624243736267},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40380859375},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.32333776354789734}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8862931132316589},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7873112559318542},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7634937763214111},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7258942127227783},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5546193718910217},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.531977117061615},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46894311904907227},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.4651331305503845},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4608108699321747},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4333624243736267},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40380859375},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.32333776354789734},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isgtasia49270.2021.9715557","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isgtasia49270.2021.9715557","pdf_url":null,"source":{"id":"https://openalex.org/S4363605822","display_name":"2021 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8687799467","display_name":null,"funder_award_id":"NRF2017EWT-EP003-061","funder_id":"https://openalex.org/F4320320743","funder_display_name":"Energy Market Authority of Singapore"}],"funders":[{"id":"https://openalex.org/F4320320743","display_name":"Energy Market Authority of Singapore","ror":"https://ror.org/05tqbd879"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1763243278","https://openalex.org/W1959608418","https://openalex.org/W2289431940","https://openalex.org/W2338128881","https://openalex.org/W2762155482","https://openalex.org/W2929637872","https://openalex.org/W2969495950","https://openalex.org/W3114070140","https://openalex.org/W3130631487","https://openalex.org/W3153872861","https://openalex.org/W4200174352","https://openalex.org/W7016021835"],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W3194885736","https://openalex.org/W4363671829","https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2912112202"],"abstract_inverted_index":{"Conventional":[0],"security":[1],"methods":[2,44],"deployed":[3],"in":[4,28,53,134,194],"power":[5,29,135,185],"plants":[6],"have":[7],"difficulty":[8],"detecting":[9,32,130,195],"time":[10,131,196],"delay":[11,132,197],"attacks,":[12,198],"since":[13],"they":[14],"do":[15,83],"not":[16,84],"alter":[17],"network":[18],"packets.":[19],"However,":[20,88],"these":[21,163],"attacks":[22,133,145],"can":[23,153],"cause":[24],"damage":[25],"and":[26,102,113,126,167],"instability":[27],"systems,":[30],"so":[31,74],"them":[33],"is":[34,76,192],"an":[35,169],"urgent":[36],"anomaly":[37,42,90,117],"detection":[38,43,91,118],"problem.":[39],"Current":[40],"state-of-the-art":[41],"employ":[45],"machine":[46],"learning":[47],"(ML)":[48],"or":[49,143],"statistical":[50],"regression":[51],"models":[52],"a":[54,115,121],"supervised":[55],"fashion,":[56],"which":[57,82,146],"require":[58],"large":[59],"amounts":[60],"of":[61,179],"labeled":[62,86],"data":[63,67],"for":[64,129,210],"training.":[65],"This":[66],"may":[68],"be":[69],"hard":[70],"to":[71,78,173],"practically":[72],"obtain,":[73],"it":[75],"preferable":[77],"use":[79,168],"unsupervised":[80,89,109],"methods,":[81],"need":[85],"data.":[87],"solutions":[92,148],"suffer":[93],"from":[94],"high":[95],"false":[96],"positive":[97],"rates,":[98],"especially":[99],"under":[100,203],"weak":[101,142],"moderate":[103,144],"attacks.":[104],"To":[105],"improve":[106],"on":[107,139,183],"existing":[108,147],"solutions,":[110],"we":[111],"develop":[112],"present":[114],"dual-stage":[116],"method":[119],"using":[120],"Recurrent":[122],"Variational":[123],"Autoencoder":[124],"(RVAE)":[125],"K-means":[127],"clustering":[128,171],"systems.":[136],"We":[137,161],"focus":[138],"samples":[140],"including":[141],"cannot":[149],"accurately":[150,175],"detect,":[151],"but":[152],"still":[154],"harm":[155],"the":[156,180,204],"system":[157],"if":[158],"strategically":[159],"targeted.":[160],"call":[162],"cases":[164],"borderline":[165,211],"samples,":[166],"additional":[170],"enhancement":[172],"more":[174],"classify":[176],"them.":[177],"Evaluation":[178],"proposed":[181],"approach":[182,191],"our":[184,190],"plant":[186],"dataset":[187],"demonstrates":[188],"that":[189],"effective":[193],"with":[199],"14.7%":[200],"higher":[201],"area":[202],"ROC":[205],"curve":[206],"(AUC)":[207],"than":[208],"RVAE":[209],"samples.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}