{"id":"https://openalex.org/W4388505134","doi":"https://doi.org/10.1109/tifs.2023.3331240","title":"TMG-GAN: Generative Adversarial Networks-Based Imbalanced Learning for Network Intrusion Detection","display_name":"TMG-GAN: Generative Adversarial Networks-Based Imbalanced Learning for Network Intrusion Detection","publication_year":2023,"publication_date":"2023-11-08","ids":{"openalex":"https://openalex.org/W4388505134","doi":"https://doi.org/10.1109/tifs.2023.3331240"},"language":"en","primary_location":{"id":"doi:10.1109/tifs.2023.3331240","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tifs.2023.3331240","pdf_url":null,"source":{"id":"https://openalex.org/S61310614","display_name":"IEEE Transactions on Information Forensics and Security","issn_l":"1556-6013","issn":["1556-6013","1556-6021"],"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 Information Forensics and Security","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/A5080389312","display_name":"Hongwei Ding","orcid":"https://orcid.org/0000-0002-0851-1994"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongwei Ding","raw_affiliation_strings":["School of Cyber Science and Engineering, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-0851-1994","affiliations":[{"raw_affiliation_string":"School of Cyber Science and Engineering, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080556931","display_name":"Yu Sun","orcid":"https://orcid.org/0000-0003-1092-9035"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yu Sun","raw_affiliation_strings":["School of Computing, National University of Singapore, Cluny Road, Singapore"],"raw_orcid":"https://orcid.org/0000-0003-1092-9035","affiliations":[{"raw_affiliation_string":"School of Computing, National University of Singapore, Cluny Road, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018752239","display_name":"Nana Huang","orcid":"https://orcid.org/0000-0003-4566-4129"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nana Huang","raw_affiliation_strings":["School of Cyber Science and Engineering, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Cyber Science and Engineering, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058161586","display_name":"Zhidong Shen","orcid":"https://orcid.org/0000-0002-4880-381X"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhidong Shen","raw_affiliation_strings":["School of Cyber Science and Engineering, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-4880-381X","affiliations":[{"raw_affiliation_string":"School of Cyber Science and Engineering, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041280931","display_name":"Xiaohui Cui","orcid":"https://orcid.org/0000-0001-6079-009X"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaohui Cui","raw_affiliation_strings":["School of Cyber Science and Engineering, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-6079-009X","affiliations":[{"raw_affiliation_string":"School of Cyber Science and Engineering, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5080389312"],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":null,"apc_paid":null,"fwci":20.1686,"has_fulltext":false,"cited_by_count":105,"citation_normalized_percentile":{"value":0.9967079,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"19","issue":null,"first_page":"1156","last_page":"1167"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9997000098228455,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.8739008903503418},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.7410131096839905},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.7055447101593018},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.5771160125732422},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4967194199562073},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.483352392911911},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4537626802921295},{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.42307043075561523},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.404890239238739},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3630675673484802},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.20606526732444763},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.19057774543762207},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.16731736063957214}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8739008903503418},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.7410131096839905},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.7055447101593018},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.5771160125732422},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4967194199562073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.483352392911911},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4537626802921295},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.42307043075561523},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.404890239238739},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3630675673484802},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.20606526732444763},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.19057774543762207},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.16731736063957214},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tifs.2023.3331240","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tifs.2023.3331240","pdf_url":null,"source":{"id":"https://openalex.org/S61310614","display_name":"IEEE Transactions on Information Forensics and Security","issn_l":"1556-6013","issn":["1556-6013","1556-6021"],"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 Information Forensics and Security","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6299999952316284}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W2759043405","https://openalex.org/W2910197492","https://openalex.org/W2921708219","https://openalex.org/W2978153077","https://openalex.org/W2982853004","https://openalex.org/W3021740526","https://openalex.org/W3029899047","https://openalex.org/W3042350893","https://openalex.org/W3048836559","https://openalex.org/W3081279800","https://openalex.org/W3084487255","https://openalex.org/W3089310265","https://openalex.org/W3104109355","https://openalex.org/W3110899937","https://openalex.org/W3111180383","https://openalex.org/W3118096520","https://openalex.org/W3148920658","https://openalex.org/W3156522613","https://openalex.org/W3157189912","https://openalex.org/W3157331533","https://openalex.org/W3165871547","https://openalex.org/W3169544834","https://openalex.org/W3195631217","https://openalex.org/W3205323312","https://openalex.org/W4200302647","https://openalex.org/W4200464085","https://openalex.org/W4206450891","https://openalex.org/W4210743106","https://openalex.org/W4212922948","https://openalex.org/W4224331011","https://openalex.org/W6741832134","https://openalex.org/W6748582592","https://openalex.org/W6778248386"],"related_works":["https://openalex.org/W3196098778","https://openalex.org/W3110074278","https://openalex.org/W2953246223","https://openalex.org/W4293320219","https://openalex.org/W3211250490","https://openalex.org/W4283584549","https://openalex.org/W2618858825","https://openalex.org/W2554314924","https://openalex.org/W2998859928","https://openalex.org/W3151498616"],"abstract_inverted_index":{"Internet":[0],"of":[1,51,104,145,158,170,216],"Things":[2],"(IoT)":[3],"devices":[4],"are":[5,41],"large":[6],"in":[7,12,38],"number,":[8],"widely":[9],"distributed,":[10],"weak":[11],"protection":[13,27],"ability,":[14],"and":[15,35,119,138,142,161,184,197,219],"vulnerable":[16],"to":[17,100],"various":[18,171],"malicious":[19],"attacks.":[20],"Intrusion":[21],"detection":[22,54,62,181,201,210],"technology":[23],"can":[24,97,115,153],"provide":[25],"good":[26,209],"for":[28],"network":[29,40],"equipment.":[30],"However,":[31],"the":[32,39,49,72,111,117,125,131,135,139,156,163,168,193,198,203,213],"normal":[33],"traffic":[34,37],"abnormal":[36],"usually":[42],"imbalanced.":[43],"Imbalanced":[44],"samples":[45,137,141,147,160],"will":[46],"seriously":[47],"affect":[48],"performance":[50],"machine":[52],"learning":[53],"algorithm.":[55],"Therefore,":[56],"this":[57],"paper":[58],"proposes":[59],"an":[60],"intrusion":[61,180,200],"method":[63,83,206],"based":[64,84,123],"on":[65,85,124,178],"data":[66,74,81,106],"augmentation,":[67],"namely":[68],"TMG-IDS.":[69],"We":[70,174],"name":[71],"proposed":[73,204],"augmentation":[75,82],"model":[76],"TMG-GAN,":[77],"which":[78,96,114,152],"is":[79],"a":[80,93,149,208],"generative":[86],"adversarial":[87],"networks":[88],"(GAN).":[89],"First,":[90],"TMG-GAN":[91],"has":[92,207],"multi-generator":[94],"structure,":[95,113],"be":[98],"used":[99],"generate":[101],"different":[102],"types":[103,144],"attack":[105],"simultaneously.":[107],"Second,":[108],"we":[109,129],"increase":[110],"classifier":[112],"optimize":[116],"generator":[118,150],"discriminator":[120],"more":[121],"efficiently":[122],"classification":[126],"loss.":[127],"Third,":[128],"calculate":[130],"cosine":[132],"similarity":[133],"between":[134,167],"generated":[136,146,159,172],"original":[140],"other":[143],"as":[148],"loss,":[151],"further":[154],"improve":[155],"quality":[157],"reduce":[162],"class":[164],"overlap":[165],"area":[166],"distributions":[169],"samples.":[173],"conduct":[175],"extensive":[176],"experiments":[177],"two":[179],"datasets,":[182],"CICIDS2017":[183],"UNSW-NB15.":[185],"The":[186],"experimental":[187],"results":[188],"show":[189],"that":[190],"compared":[191],"with":[192],"advanced":[194],"oversampling":[195],"algorithm":[196],"latest":[199],"algorithm,":[202],"TMG-IDS":[205],"effect":[211],"under":[212],"three":[214],"indicators":[215],"Precision,":[217],"Recall":[218],"F1-score.":[220]},"counts_by_year":[{"year":2026,"cited_by_count":22},{"year":2025,"cited_by_count":65},{"year":2024,"cited_by_count":18}],"updated_date":"2026-05-23T08:51:43.019350","created_date":"2025-10-10T00:00:00"}
