{"id":"https://openalex.org/W4390394385","doi":"https://doi.org/10.1109/isgtasia54891.2023.10372697","title":"A Framework for Generating Attack Samples in Power Energy System using VAEs","display_name":"A Framework for Generating Attack Samples in Power Energy System using VAEs","publication_year":2023,"publication_date":"2023-11-21","ids":{"openalex":"https://openalex.org/W4390394385","doi":"https://doi.org/10.1109/isgtasia54891.2023.10372697"},"language":"en","primary_location":{"id":"doi:10.1109/isgtasia54891.2023.10372697","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/isgtasia54891.2023.10372697","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 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/A5101285793","display_name":"Tran L.T Le","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tran L.T Le","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063936654","display_name":"David K. Y. Yau","orcid":"https://orcid.org/0000-0001-9061-7423"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"David K.Y Yau","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5087008513","display_name":"Justin Albrethsen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Justin Albrethsen","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101285793"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22328212,"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/T10917","display_name":"Smart Grid Security and Resilience","score":0.9979000091552734,"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"}},"topics":[{"id":"https://openalex.org/T10917","display_name":"Smart Grid Security and Resilience","score":0.9979000091552734,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9894000291824341,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9567999839782715,"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":0.8406141400337219},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7917152643203735},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.569611132144928},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5547298192977905},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5441973209381104},{"id":"https://openalex.org/keywords/attack-model","display_name":"Attack model","score":0.5100939869880676},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.49925875663757324},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4738740622997284},{"id":"https://openalex.org/keywords/attack-patterns","display_name":"Attack patterns","score":0.43032291531562805},{"id":"https://openalex.org/keywords/electric-power-system","display_name":"Electric power system","score":0.4270508587360382},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42162904143333435},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3782590329647064},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.2524396479129791},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.22718492150306702},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.1944417655467987}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8406141400337219},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7917152643203735},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.569611132144928},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5547298192977905},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5441973209381104},{"id":"https://openalex.org/C65856478","wikidata":"https://www.wikidata.org/wiki/Q3991682","display_name":"Attack model","level":2,"score":0.5100939869880676},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49925875663757324},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4738740622997284},{"id":"https://openalex.org/C2780741293","wikidata":"https://www.wikidata.org/wiki/Q4818019","display_name":"Attack patterns","level":3,"score":0.43032291531562805},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.4270508587360382},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42162904143333435},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3782590329647064},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.2524396479129791},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.22718492150306702},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.1944417655467987},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isgtasia54891.2023.10372697","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/isgtasia54891.2023.10372697","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.7799999713897705,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2909960414","https://openalex.org/W2929637872","https://openalex.org/W3043084043","https://openalex.org/W3182952669","https://openalex.org/W4200174352","https://openalex.org/W4214608799"],"related_works":["https://openalex.org/W4321789545","https://openalex.org/W2357769287","https://openalex.org/W3194041658","https://openalex.org/W2090692546","https://openalex.org/W2103128397","https://openalex.org/W2182073463","https://openalex.org/W2767556742","https://openalex.org/W926080196","https://openalex.org/W2357161115","https://openalex.org/W2143949933"],"abstract_inverted_index":{"Real-world":[0],"datasets":[1],"from":[2,93],"the":[3,18,37,94,160,167,212,223],"cyber-physical":[4],"systems":[5,42],"(CPS)":[6],"are":[7],"indeed":[8],"valuable":[9],"for":[10,35,51,146,178],"training":[11,190],"and":[12,23,29,43,60,97,115,138,164,175,202,207],"evaluating":[13],"machine":[14],"learning":[15],"models":[16],"in":[17,120,162,199,222],"context":[19],"of":[20,39,166,211],"cybersecurity":[21],"analysis":[22],"defense":[24,66],"against":[25,73],"attacks.":[26,75],"Generating":[27],"realistic":[28],"practical":[30],"attack":[31,53,91,102,122],"samples":[32,54],"is":[33],"essential":[34],"understanding":[36],"vulnerabilities":[38],"power":[40,224],"energy":[41,225],"developing":[44],"more":[45,58,71,173],"effective":[46,72,119],"defenses.":[47],"However,":[48],"existing":[49],"methods":[50],"generating":[52,147],"must":[55],"often":[56],"be":[57,70],"balanced":[59],"realistic.":[61],"This":[62],"can":[63],"lead":[64],"to":[65,88,110,171],"techniques":[67],"that":[68,189],"could":[69],"real-world":[74],"Our":[76,157],"work":[77,187],"proposes":[78],"a":[79,129,172,192],"temporal":[80,113],"convolutional":[81],"network":[82],"(TCN)":[83],"based":[84],"variational":[85],"autoencoder":[86],"(VAE)":[87],"learn":[89],"disentangled":[90],"characteristics":[92],"real":[95],"dataset":[96,177,194],"generate":[98,149],"diverse":[99,176],"time":[100],"delay":[101],"(TDA)":[103],"sample":[104],"sets.":[105],"The":[106],"TCN-VAE":[107],"model\u2019s":[108],"ability":[109],"capture":[111],"complex":[112],"patterns":[114],"dependencies":[116],"make":[117],"it":[118],"representing":[121],"features.":[123,156],"In":[124],"addition,":[125],"integrating":[126],"XGBoost":[127],"as":[128],"classifier":[130],"model,":[131],"we":[132],"compute":[133],"correlation":[134],"information":[135],"between":[136],"features":[137],"latent":[139],"dimensions,":[140],"gaining":[141],"insights":[142],"into":[143,219],"feature":[144],"importance":[145],"automatic":[148],"control":[150],"(AGC)":[151],"frequency":[152,217],"with":[153,195],"desired":[154],"characteristic":[155],"framework":[158],"addresses":[159],"imbalance":[161],"quantity":[163],"quality":[165,197],"original":[168],"dataset,":[169],"leading":[170],"comprehensive":[174],"improved":[179],"model":[180],"performance.":[181],"Through":[182],"two":[183],"use":[184],"cases,":[185],"our":[186],"demonstrates":[188],"on":[191],"large":[193],"good":[196],"results":[198],"better":[200],"performance":[201],"higher":[203],"correct":[204,208],"classification":[205],"accuracy":[206,210],"estimation":[209],"cycle":[213],"at":[214],"which":[215],"AGC":[216],"steps":[218],"unsafe":[220],"levels":[221],"system.":[226]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}
