{"id":"https://openalex.org/W7160277198","doi":"https://doi.org/10.1109/tccn.2026.3689845","title":"LSGLM: An Efficient Threat Detection Method Based on Large-Scale Graph Parallelized Learning","display_name":"LSGLM: An Efficient Threat Detection Method Based on Large-Scale Graph Parallelized Learning","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7160277198","doi":"https://doi.org/10.1109/tccn.2026.3689845"},"language":null,"primary_location":{"id":"doi:10.1109/tccn.2026.3689845","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tccn.2026.3689845","pdf_url":null,"source":{"id":"https://openalex.org/S2484188435","display_name":"IEEE Transactions on Cognitive Communications and Networking","issn_l":"2332-7731","issn":["2332-7731","2372-2045"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Cognitive Communications and Networking","raw_type":"journal-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/A5035236310","display_name":"Yiqing Luo","orcid":"https://orcid.org/0000-0002-9122-4821"},"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":"Yiqing Luo","raw_affiliation_strings":["School of Electronic Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9122-4821","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049069235","display_name":"Mingshu He","orcid":"https://orcid.org/0000-0002-2896-4595"},"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":"Mingshu He","raw_affiliation_strings":["School of Cyberspace Security and the Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications (BUPT), Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2896-4595","affiliations":[{"raw_affiliation_string":"School of Cyberspace Security and the Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications (BUPT), Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087578136","display_name":"XM. Wang","orcid":null},"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":"Xiaojuan Wang","raw_affiliation_strings":["School of Cyberspace Security, Beijing University of Posts and Telecommunications (BUPT), Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3490-963X","affiliations":[{"raw_affiliation_string":"School of Cyberspace Security, Beijing University of Posts and Telecommunications (BUPT), Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.53438328,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"7831","last_page":"7844"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.48739999532699585,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.48739999532699585,"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/T10734","display_name":"Information and Cyber Security","score":0.0737999975681305,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.051600001752376556,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5547999739646912},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5471000075340271},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4788999855518341},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4377000033855438},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.4246000051498413},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41260001063346863},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.35600000619888306},{"id":"https://openalex.org/keywords/subgraph-isomorphism-problem","display_name":"Subgraph isomorphism problem","score":0.3528999984264374},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.34279999136924744}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8776999711990356},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5547999739646912},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5471000075340271},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4788999855518341},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4415999948978424},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4377000033855438},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.4246000051498413},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42340001463890076},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41260001063346863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39399999380111694},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3781999945640564},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.35600000619888306},{"id":"https://openalex.org/C131992880","wikidata":"https://www.wikidata.org/wiki/Q2528185","display_name":"Subgraph isomorphism problem","level":3,"score":0.3528999984264374},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.34279999136924744},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.3409999907016754},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3328999876976013},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.3246999979019165},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.319599986076355},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C53811970","wikidata":"https://www.wikidata.org/wiki/Q5062194","display_name":"Centrality","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tccn.2026.3689845","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tccn.2026.3689845","pdf_url":null,"source":{"id":"https://openalex.org/S2484188435","display_name":"IEEE Transactions on Cognitive Communications and Networking","issn_l":"2332-7731","issn":["2332-7731","2372-2045"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Cognitive Communications and Networking","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.44590815901756287,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G3904271281","display_name":null,"funder_award_id":"2025KYQD17(BUPT)","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G6027343793","display_name":null,"funder_award_id":"62227805","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7212251950","display_name":null,"funder_award_id":"62402053","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7586292810","display_name":null,"funder_award_id":"2025KYQD17","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Network":[0],"threat":[1,34,39,63],"detection":[2,40,64,210,240],"and":[3,25,45,87,95,108,146,186,211,246,256],"identification":[4],"remains":[5],"a":[6,99,128,204,267],"fundamental":[7],"task":[8],"in":[9,30],"the":[10,31,80,120,148,161,192,221,224,232,237,249],"defense":[11],"of":[12,17,33,83,123,200,223,242],"cyberspace.":[13],"Graphs":[14],"are":[15,42],"capable":[16],"capturing":[18],"rich":[19],"structural":[20],"information":[21],"while":[22,261],"retaining":[23],"content,":[24],"they":[26],"have":[27,219],"wide":[28],"applications":[29],"field":[32],"detection.":[35],"However,":[36],"existing":[37,124,164],"graph-based":[38],"approaches":[41,167],"complex,":[43],"time-consuming,":[44],"challenging":[46],"to":[47,49,72,97,118],"apply":[48],"real":[50],"network":[51,84],"scenarios":[52],"with":[53],"invisible":[54],"or":[55],"incomplete":[56],"topologies.":[57],"Therefore,":[58],"we":[59,171,195],"propose":[60,172],"an":[61],"efficient":[62],"method":[65,78,225,238],"based":[66,142,213],"on":[67,143,214,248],"large-scale":[68],"graph":[69,125,152,165],"parallelized":[70],"learning":[71,126,166,208],"address":[73],"these":[74],"application":[75],"difficulties.":[76],"This":[77,135],"addresses":[79],"potential":[81],"challenges":[82],"topology":[85],"invisibility":[86],"incompleteness":[88],"by":[89],"leveraging":[90],"bidirectional":[91],"inference":[92],"between":[93,111],"flow":[94],"behavior":[96,209],"construct":[98],"semantic":[100,150],"similarity":[101,109,151],"graph,":[102],"where":[103],"flows":[104,112],"serve":[105,113],"as":[106,114,266],"nodes":[107,141],"relationships":[110],"edges.":[115],"In":[116],"order":[117],"improve":[119,159],"low":[121],"efficiency":[122,265],"approaches,":[127],"non-linked":[129],"subgraph":[130,193],"sampling":[131],"mechanism":[132,136],"is":[133,271],"introduced.":[134],"removes":[137],"edge":[138],"connections,":[139],"induces":[140],"intimate":[144],"relationships,":[145],"divides":[147],"constructed":[149],"into":[153,203],"subgraphs":[154],"for":[155,207],"parallel":[156],"learning.":[157],"To":[158],"upon":[160],"issue":[162],"that":[163,236],"embed":[168],"single":[169],"representations,":[170],"four":[173,198,227],"feature":[174,178,201],"embedding":[175],"modes:":[176],"original":[177],"embedding,":[179,182,185],"global":[180],"role":[181],"area":[183],"position":[184],"balanced":[187],"distance":[188],"embedding.":[189],"Subsequently,":[190],"at":[191],"level,":[194],"input":[196],"nodes\u2019":[197],"types":[199],"embeddings":[202],"BERT":[205],"model":[206],"classification":[212],"updated":[215],"node":[216],"representations.":[217],"We":[218],"validated":[220],"efficacy":[222],"across":[226],"publicly":[228],"available":[229,272],"datasets.":[230],"Furthermore,":[231],"evaluation":[233],"results":[234],"demonstrate":[235],"achieved":[239],"accuracies":[241],"97.84%,":[243],"98.46%,":[244],"100%,":[245],"96.59%":[247],"ISCX":[250],"IDS2012,":[251],"CIC":[252,254],"DDoS2019,":[253],"IOT2022,":[255],"USTC-TFC2016":[257],"public":[258],"datasets":[259],"respectively,":[260],"also":[262],"exhibiting":[263],"high":[264],"characteristic.":[268],"The":[269],"code":[270],"on:":[273],"https://github.com/sixteen23333/LSGLM.":[274]},"counts_by_year":[],"updated_date":"2026-05-08T13:12:06.581006","created_date":"2026-05-06T00:00:00"}