{"id":"https://openalex.org/W4414229466","doi":"https://doi.org/10.1109/access.2025.3609783","title":"STGVAD: Spatio-Temporal Graph-Based Vessel Behavior Anomaly Detection","display_name":"STGVAD: Spatio-Temporal Graph-Based Vessel Behavior Anomaly Detection","publication_year":2025,"publication_date":"2025-09-16","ids":{"openalex":"https://openalex.org/W4414229466","doi":"https://doi.org/10.1109/access.2025.3609783"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3609783","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3609783","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3609783","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103534717","display_name":"J.J. Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jeehong Kim","raw_affiliation_strings":["Graduate School of Data Science, Seoul National University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0009-0002-3426-842X","affiliations":[{"raw_affiliation_string":"Graduate School of Data Science, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Minchan Kim","orcid":"https://orcid.org/0009-0009-8776-4209"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Minchan Kim","raw_affiliation_strings":["Graduate School of Data Science, Seoul National University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0009-0009-8776-4209","affiliations":[{"raw_affiliation_string":"Graduate School of Data Science, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112645144","display_name":"Young-Seok Hwang","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Youngseok Hwang","raw_affiliation_strings":["Graduate School of Data Science, Seoul National University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0009-0007-1461-2445","affiliations":[{"raw_affiliation_string":"Graduate School of Data Science, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Sungho Bae","orcid":"https://orcid.org/0009-0000-9247-5318"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sungho Bae","raw_affiliation_strings":["Graduate School of Data Science, Seoul National University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0009-0000-9247-5318","affiliations":[{"raw_affiliation_string":"Graduate School of Data Science, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073940573","display_name":"Deuk-Jae Cho","orcid":"https://orcid.org/0000-0001-5088-5175"},"institutions":[{"id":"https://openalex.org/I4210102567","display_name":"Global Core Research Center for Ships and Offshore Plants","ror":"https://ror.org/011kmvk73","country_code":"KR","type":"other","lineage":["https://openalex.org/I4210102567"]},{"id":"https://openalex.org/I4399657955","display_name":"Korea Research Institute of Ships and Ocean Engineering","ror":"https://ror.org/0046ap498","country_code":null,"type":"funder","lineage":["https://openalex.org/I4399657955"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Deuk Jae Cho","raw_affiliation_strings":["Korea Research Institute of Ships and Ocean Engineering, Daejeon, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-5088-5175","affiliations":[{"raw_affiliation_string":"Korea Research Institute of Ships and Ocean Engineering, Daejeon, South Korea","institution_ids":["https://openalex.org/I4210102567","https://openalex.org/I4399657955"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100769973","display_name":"Won Hee Lee","orcid":"https://orcid.org/0000-0001-8534-9639"},"institutions":[{"id":"https://openalex.org/I4210102567","display_name":"Global Core Research Center for Ships and Offshore Plants","ror":"https://ror.org/011kmvk73","country_code":"KR","type":"other","lineage":["https://openalex.org/I4210102567"]},{"id":"https://openalex.org/I4399657955","display_name":"Korea Research Institute of Ships and Ocean Engineering","ror":"https://ror.org/0046ap498","country_code":null,"type":"funder","lineage":["https://openalex.org/I4399657955"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Wonhee Lee","raw_affiliation_strings":["Korea Research Institute of Ships and Ocean Engineering, Daejeon, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea Research Institute of Ships and Ocean Engineering, Daejeon, South Korea","institution_ids":["https://openalex.org/I4210102567","https://openalex.org/I4399657955"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100699674","display_name":"Hyunwoo Park","orcid":"https://orcid.org/0000-0001-9818-217X"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyunwoo Park","raw_affiliation_strings":["Graduate School of Data Science, Seoul National University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-9818-217X","affiliations":[{"raw_affiliation_string":"Graduate School of Data Science, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.7588,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.88547759,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"14","issue":null,"first_page":"2152","last_page":"2165"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9965999722480774,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9965999722480774,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9718000292778015,"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/T12127","display_name":"Software System Performance and Reliability","score":0.9713000059127808,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6205000281333923},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5630999803543091},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.557200014591217},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5527999997138977},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.4212999939918518},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.36500000953674316},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.35339999198913574},{"id":"https://openalex.org/keywords/clustering-coefficient","display_name":"Clustering coefficient","score":0.3303000032901764}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7727000117301941},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6205000281333923},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5630999803543091},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.557200014591217},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5527999997138977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42149999737739563},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.4212999939918518},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41909998655319214},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3718999922275543},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33059999346733093},{"id":"https://openalex.org/C22047676","wikidata":"https://www.wikidata.org/wiki/Q898680","display_name":"Clustering coefficient","level":3,"score":0.3303000032901764},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.32330000400543213},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.31279999017715454},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.27000001072883606},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2025.3609783","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3609783","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3609783","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3609783","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5509363659","display_name":null,"funder_award_id":"PES5580","funder_id":"https://openalex.org/F4320322011","funder_display_name":"Ministry of Oceans and Fisheries"},{"id":"https://openalex.org/G859900322","display_name":null,"funder_award_id":"2520000291","funder_id":"https://openalex.org/F4320328008","funder_display_name":"Korea Research Institute of Ships and Ocean Engineering"},{"id":"https://openalex.org/G8791210879","display_name":null,"funder_award_id":"2520000685","funder_id":"https://openalex.org/F4320322011","funder_display_name":"Ministry of Oceans and Fisheries"}],"funders":[{"id":"https://openalex.org/F4320322011","display_name":"Ministry of Oceans and Fisheries","ror":"https://ror.org/00c5s4a53"},{"id":"https://openalex.org/F4320328008","display_name":"Korea Research Institute of Ships and Ocean Engineering","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1737218281","https://openalex.org/W1999124146","https://openalex.org/W2064675550","https://openalex.org/W2083442964","https://openalex.org/W2160642098","https://openalex.org/W2161553053","https://openalex.org/W2205875912","https://openalex.org/W2212766728","https://openalex.org/W2265620001","https://openalex.org/W2577404749","https://openalex.org/W2609535044","https://openalex.org/W2879698168","https://openalex.org/W2886960696","https://openalex.org/W2903871660","https://openalex.org/W2925090368","https://openalex.org/W2936061594","https://openalex.org/W2964015378","https://openalex.org/W2965341826","https://openalex.org/W2990822052","https://openalex.org/W2999309192","https://openalex.org/W3015809448","https://openalex.org/W3035096461","https://openalex.org/W3093261329","https://openalex.org/W3184022454","https://openalex.org/W3187117672","https://openalex.org/W3208688400","https://openalex.org/W4200347067","https://openalex.org/W4205214170","https://openalex.org/W4206338773","https://openalex.org/W4211102415","https://openalex.org/W4213417656","https://openalex.org/W4224320982","https://openalex.org/W4283817628","https://openalex.org/W4285343669","https://openalex.org/W4289829872","https://openalex.org/W4382318991","https://openalex.org/W4385245566","https://openalex.org/W4385568303","https://openalex.org/W4386434910","https://openalex.org/W4388660504","https://openalex.org/W4388793955","https://openalex.org/W4390576778","https://openalex.org/W4392033237","https://openalex.org/W4393253307","https://openalex.org/W4399759252","https://openalex.org/W4403807805","https://openalex.org/W4408418328","https://openalex.org/W4409209436","https://openalex.org/W4410701742"],"related_works":[],"abstract_inverted_index":{"Predicting":[0],"vessel":[1,88,102],"trajectories":[2],"and":[3,21,41,107,119,136,196,200],"detecting":[4],"anomalous":[5],"behaviors":[6],"are":[7,59],"fundamental":[8],"yet":[9],"challenging":[10],"tasks":[11],"in":[12,38,205],"the":[13,19,25,124,130,190,198],"maritime":[14,54,207],"domain,":[15],"primarily":[16],"due":[17],"to":[18,53,150],"dynamic":[20],"unstructured":[22],"nature":[23],"of":[24,92,133,192,202],"marine":[26],"environment.":[27],"While":[28],"Spatio-Temporal":[29],"Graph":[30],"Neural":[31],"Networks":[32],"(ST-GNNs)":[33],"have":[34],"shown":[35],"great":[36],"promise":[37],"modeling":[39,132,195],"relational":[40],"temporal":[42,134],"dependencies,":[43],"their":[44,51],"reliance":[45],"on":[46,94],"fixed":[47],"spatial":[48,96,121],"anchors":[49,58],"limits":[50],"applicability":[52],"scenarios,":[55],"where":[56],"such":[57],"inherently":[60],"absent.":[61],"To":[62,159],"address":[63],"this":[64],"limitation,":[65],"we":[66,98,163],"propose":[67],"a":[68,105,109,140,147,156],"novel":[69],"GNN-based":[70],"framework,":[71],"denoted":[72],"as":[73,104],"<italic":[74],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[75],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Spatio-Temporal":[76],"Graph-based":[77],"Vessel":[78],"behavior":[79,89],"Anomaly":[80],"Detection</i>":[81],"(STGVAD),":[82],"that":[83,174],"redefines":[84],"graph":[85,113,142],"construction":[86],"for":[87],"modeling.":[90],"Instead":[91],"relying":[93],"static":[95],"points,":[97],"represent":[99],"each":[100],"timestamped":[101],"state":[103],"node":[106],"construct":[108],"unified":[110],"multi-ship":[111],"trajectory":[112],"by":[114,155],"linking":[115],"temporally":[116],"adjacent":[117],"nodes":[118],"incorporating":[120],"proximity":[122],"using":[123],"OPTICS":[125],"clustering":[126],"algorithm.":[127],"This":[128],"enables":[129],"joint":[131,193],"dynamics":[135],"inter-vessel":[137],"interactions":[138],"within":[139],"single":[141],"structure.":[143],"Our":[144],"framework":[145],"employs":[146],"GNN":[148],"encoder":[149],"capture":[151],"spatio-temporal":[152,194],"patterns,":[153],"followed":[154],"time-series":[157,179],"module.":[158],"support":[160],"objective":[161],"evaluation,":[162],"manually":[164],"injected":[165],"anomalies":[166],"into":[167],"real":[168],"AIS":[169],"dataset.":[170],"Experimental":[171],"results":[172],"demonstrate":[173],"STGVAD":[175],"significantly":[176],"outperforms":[177],"conventional":[178],"baselines,":[180],"particularly":[181],"under":[182],"conditions":[183],"with":[184],"scarce":[185],"anomalies.":[186],"These":[187],"findings":[188],"highlight":[189],"importance":[191],"validate":[197],"robustness":[199],"generalizability":[201],"our":[203],"approach":[204],"complex":[206],"environments.":[208]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}