{"id":"https://openalex.org/W4414463918","doi":"https://doi.org/10.1109/bmsb65076.2025.11165544","title":"A Multi-Dimensional Telecom Fraud Detection Model Based on Graph Attention Networks","display_name":"A Multi-Dimensional Telecom Fraud Detection Model Based on Graph Attention Networks","publication_year":2025,"publication_date":"2025-06-11","ids":{"openalex":"https://openalex.org/W4414463918","doi":"https://doi.org/10.1109/bmsb65076.2025.11165544"},"language":"en","primary_location":{"id":"doi:10.1109/bmsb65076.2025.11165544","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bmsb65076.2025.11165544","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)","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/A5054435331","display_name":"Chenbin Zhao","orcid":"https://orcid.org/0000-0002-2390-7153"},"institutions":[{"id":"https://openalex.org/I6507939","display_name":"China United Network Communications Group (China)","ror":"https://ror.org/028w99c90","country_code":"CN","type":"company","lineage":["https://openalex.org/I6507939"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chenbin Zhao","raw_affiliation_strings":["China United Network Communications Group Co., Ltd.,Beijing,China"],"affiliations":[{"raw_affiliation_string":"China United Network Communications Group Co., Ltd.,Beijing,China","institution_ids":["https://openalex.org/I6507939"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100722719","display_name":"Yuan Gao","orcid":"https://orcid.org/0000-0002-0507-6499"},"institutions":[{"id":"https://openalex.org/I6507939","display_name":"China United Network Communications Group (China)","ror":"https://ror.org/028w99c90","country_code":"CN","type":"company","lineage":["https://openalex.org/I6507939"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhao Gao","raw_affiliation_strings":["China United Network Communications Group Co., Ltd.,Beijing,China"],"affiliations":[{"raw_affiliation_string":"China United Network Communications Group Co., Ltd.,Beijing,China","institution_ids":["https://openalex.org/I6507939"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100759781","display_name":"Ying Xing","orcid":"https://orcid.org/0000-0003-1965-0786"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Xing","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101808287","display_name":"Yang Ke","orcid":"https://orcid.org/0000-0002-5578-0302"},"institutions":[{"id":"https://openalex.org/I6507939","display_name":"China United Network Communications Group (China)","ror":"https://ror.org/028w99c90","country_code":"CN","type":"company","lineage":["https://openalex.org/I6507939"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Ke","raw_affiliation_strings":["China United Network Communications Group Co., Ltd.,Beijing,China"],"affiliations":[{"raw_affiliation_string":"China United Network Communications Group Co., Ltd.,Beijing,China","institution_ids":["https://openalex.org/I6507939"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100337435","display_name":"Xin Tian","orcid":"https://orcid.org/0000-0002-6515-3599"},"institutions":[{"id":"https://openalex.org/I6507939","display_name":"China United Network Communications Group (China)","ror":"https://ror.org/028w99c90","country_code":"CN","type":"company","lineage":["https://openalex.org/I6507939"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Tian","raw_affiliation_strings":["China United Network Communications Group Co., Ltd.,Beijing,China"],"affiliations":[{"raw_affiliation_string":"China United Network Communications Group Co., Ltd.,Beijing,China","institution_ids":["https://openalex.org/I6507939"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102721894","display_name":"Haoran Chen","orcid":"https://orcid.org/0009-0004-1562-7495"},"institutions":[{"id":"https://openalex.org/I4210152501","display_name":"China Power Engineering Consulting Group (China)","ror":"https://ror.org/04t59qd02","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210152501"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoran Chen","raw_affiliation_strings":["China Information Technology Designing &Consulting Institute Co.,Ltd,Beijing,China"],"affiliations":[{"raw_affiliation_string":"China Information Technology Designing &Consulting Institute Co.,Ltd,Beijing,China","institution_ids":["https://openalex.org/I4210152501"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5054435331"],"corresponding_institution_ids":["https://openalex.org/I6507939"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14294769,"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":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9649999737739563,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9649999737739563,"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/mobile-phone","display_name":"Mobile phone","score":0.4999000132083893},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4982999861240387},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4731000065803528},{"id":"https://openalex.org/keywords/phone","display_name":"Phone","score":0.4555000066757202},{"id":"https://openalex.org/keywords/call-graph","display_name":"Call graph","score":0.45210000872612},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.39309999346733093},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.3853999972343445},{"id":"https://openalex.org/keywords/telecommunications-network","display_name":"Telecommunications network","score":0.37959998846054077}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7699000239372253},{"id":"https://openalex.org/C2777421447","wikidata":"https://www.wikidata.org/wiki/Q17517","display_name":"Mobile phone","level":2,"score":0.4999000132083893},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4982999861240387},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4731000065803528},{"id":"https://openalex.org/C2778707766","wikidata":"https://www.wikidata.org/wiki/Q202064","display_name":"Phone","level":2,"score":0.4555000066757202},{"id":"https://openalex.org/C102379954","wikidata":"https://www.wikidata.org/wiki/Q2589940","display_name":"Call graph","level":2,"score":0.45210000872612},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.42410001158714294},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4187999963760376},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.40230000019073486},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.39309999346733093},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.3853999972343445},{"id":"https://openalex.org/C192126672","wikidata":"https://www.wikidata.org/wiki/Q1068715","display_name":"Telecommunications network","level":2,"score":0.37959998846054077},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3619999885559082},{"id":"https://openalex.org/C95491727","wikidata":"https://www.wikidata.org/wiki/Q992968","display_name":"Mobile telephony","level":3,"score":0.3537999987602234},{"id":"https://openalex.org/C59201141","wikidata":"https://www.wikidata.org/wiki/Q46904","display_name":"GSM","level":2,"score":0.33980000019073486},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.30970001220703125},{"id":"https://openalex.org/C34947359","wikidata":"https://www.wikidata.org/wiki/Q665189","display_name":"Complex network","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3077000081539154},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2994999885559082},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C153646914","wikidata":"https://www.wikidata.org/wiki/Q535695","display_name":"Cellular network","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2637999951839447},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bmsb65076.2025.11165544","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bmsb65076.2025.11165544","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)","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":18,"referenced_works":["https://openalex.org/W2079450376","https://openalex.org/W2089014283","https://openalex.org/W2909682623","https://openalex.org/W2954152666","https://openalex.org/W3035922394","https://openalex.org/W3068123808","https://openalex.org/W3091722910","https://openalex.org/W3153858161","https://openalex.org/W4206991245","https://openalex.org/W4289260202","https://openalex.org/W4294343952","https://openalex.org/W4360996447","https://openalex.org/W4379744863","https://openalex.org/W4385938146","https://openalex.org/W4387969277","https://openalex.org/W4391557694","https://openalex.org/W4399620390","https://openalex.org/W4410738219"],"related_works":[],"abstract_inverted_index":{"With":[0,54],"the":[1,15,32,55,127,178],"rapid":[2],"development":[3],"of":[4,17,57,180],"5G":[5],"mobile":[6],"networks,":[7],"telecom":[8,24,63,76],"fraud":[9,25,64,77],"methods":[10,27,66],"are":[11],"continuously":[12],"evolving,":[13],"and":[14,19,42,118,149,159,173],"number":[16],"fraudsters":[18],"victims":[20],"is":[21],"increasing.":[22],"Traditional":[23],"detection":[26,65,78,171],"struggle":[28],"to":[29,93,112,152],"effectively":[30],"capture":[31],"complex":[33,47],"relationships":[34,48],"between":[35],"nodes":[36],"when":[37],"dealing":[38],"with":[39],"high-dimensional,":[40],"complex,":[41],"dynamic":[43],"telecommunication":[44],"data.":[45,182],"These":[46],"can":[49],"be":[50],"modelled":[51],"as":[52,134],"graphs.":[53],"emergence":[56],"Graph":[58,82,89],"Neural":[59,90],"Networks":[60,84,91],"(GNNs),":[61],"GNN-based":[62],"have":[67],"gradually":[68],"become":[69],"a":[70,75,108,154],"research":[71],"hotspot.":[72],"We":[73],"present":[74],"model":[79,87,106,168],"based":[80],"on":[81],"Attention":[83],"(GAT).":[85],"The":[86],"leverages":[88],"advantages":[92],"enhance":[94],"node":[95,115],"feature":[96],"representations":[97],"by":[98],"adaptively":[99],"aggregating":[100],"neighbouring":[101],"nodes\u2019":[102],"features.":[103],"Then,":[104],"our":[105,167],"employs":[107],"Multi-Layer":[109],"Perceptron":[110],"(MLP)":[111],"classify":[113],"each":[114],"(i.e.,":[116],"user)":[117],"determine":[119],"its":[120],"involvement":[121],"in":[122,176],"fraudulent":[123],"activities.":[124],"In":[125],"constructing":[126],"model,":[128],"we":[129],"incorporate":[130],"multi-dimensional":[131,181],"data":[132],"such":[133],"phone":[135],"numbers,":[136],"call":[137],"records":[138],"(e.g.,":[139],"participants,":[140],"duration,":[141],"type,":[142],"time,":[143],"location),":[144],"billing":[145],"information,":[146],"devices":[147],"used,":[148],"user":[150],"IDs":[151],"form":[153],"graph":[155],"structure":[156],"representing":[157],"users":[158],"their":[160],"communication":[161],"behaviours.":[162],"Experimental":[163],"results":[164],"demonstrate":[165],"that":[166],"significantly":[169],"improves":[170],"accuracy":[172],"robustness,":[174],"particularly":[175],"handling":[177],"challenges":[179]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}