{"id":"https://openalex.org/W3210436151","doi":"https://doi.org/10.1109/icspcc52875.2021.9564824","title":"Network Malicious Traffic Identification Method Based On WGAN Category Balancing","display_name":"Network Malicious Traffic Identification Method Based On WGAN Category Balancing","publication_year":2021,"publication_date":"2021-08-17","ids":{"openalex":"https://openalex.org/W3210436151","doi":"https://doi.org/10.1109/icspcc52875.2021.9564824","mag":"3210436151"},"language":"en","primary_location":{"id":"doi:10.1109/icspcc52875.2021.9564824","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icspcc52875.2021.9564824","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5024572660","display_name":"Anzhou Wang","orcid":"https://orcid.org/0000-0001-9377-9364"},"institutions":[{"id":"https://openalex.org/I4210125346","display_name":"Gansu Institute of Political Science and Law","ror":"https://ror.org/025km4h87","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210125346"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Anzhou Wang","raw_affiliation_strings":["Academy for Cyberspace Security, Gansu University of Political Science and Law, Lanzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Academy for Cyberspace Security, Gansu University of Political Science and Law, Lanzhou, China","institution_ids":["https://openalex.org/I4210125346"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100527845","display_name":"Yaojun Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I4210125346","display_name":"Gansu Institute of Political Science and Law","ror":"https://ror.org/025km4h87","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210125346"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaojun Ding","raw_affiliation_strings":["Academy for Cyberspace Security, Gansu University of Political Science and Law, Lanzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Academy for Cyberspace Security, Gansu University of Political Science and Law, Lanzhou, China","institution_ids":["https://openalex.org/I4210125346"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210125346"],"apc_list":null,"apc_paid":null,"fwci":0.1468,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.41947044,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9968000054359436,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9968000054359436,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9921000003814697,"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":0.9890000224113464,"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/oversampling","display_name":"Oversampling","score":0.8638005256652832},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7745975255966187},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6698416471481323},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5822054743766785},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.49878859519958496},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.49000969529151917},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4883279502391815},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.4512070119380951},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44293251633644104},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.42387253046035767},{"id":"https://openalex.org/keywords/grayscale","display_name":"Grayscale","score":0.41027626395225525},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3622015118598938},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3291468024253845},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.24617180228233337},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.14269781112670898}],"concepts":[{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.8638005256652832},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7745975255966187},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6698416471481323},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5822054743766785},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49878859519958496},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.49000969529151917},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4883279502391815},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.4512070119380951},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44293251633644104},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.42387253046035767},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.41027626395225525},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3622015118598938},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3291468024253845},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.24617180228233337},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.14269781112670898},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"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":2,"locations":[{"id":"doi:10.1109/icspcc52875.2021.9564824","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icspcc52875.2021.9564824","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","raw_type":"proceedings-article"},{"id":"pmh:oai:ir.lzu.edu.cn/:262010/468387","is_oa":false,"landing_page_url":"http://ir.lzu.edu.cn/handle/262010/468387","pdf_url":null,"source":{"id":"https://openalex.org/S4406923049","display_name":"Lanzhou University Institutional Repository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"\u4f1a\u8bae\u8bba\u6587"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6053287198","display_name":null,"funder_award_id":"2019040","funder_id":"https://openalex.org/F4320327659","funder_display_name":"Gansu University of Chinese Medicine"}],"funders":[{"id":"https://openalex.org/F4320327659","display_name":"Gansu University of Chinese Medicine","ror":"https://ror.org/00g741v42"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1710476689","https://openalex.org/W1941659294","https://openalex.org/W2085963752","https://openalex.org/W2112796928","https://openalex.org/W2567456068","https://openalex.org/W2581637843","https://openalex.org/W2746273737","https://openalex.org/W2773315559","https://openalex.org/W2990714546","https://openalex.org/W3045858585"],"related_works":["https://openalex.org/W2012117566","https://openalex.org/W1981856929","https://openalex.org/W2989490741","https://openalex.org/W3092506759","https://openalex.org/W2367545121","https://openalex.org/W4248881655","https://openalex.org/W2482165163","https://openalex.org/W3010890513","https://openalex.org/W120741642","https://openalex.org/W138569904"],"abstract_inverted_index":{"Aiming":[0],"at":[1],"the":[2,32,35,53,62,68,80,84,121,125,152,156,162,171,174,181],"problem":[3,54],"of":[4,19,34,47,55,86,137,173],"data":[5,38,48,56,60,66],"imbalance":[6],"when":[7],"in":[8,67],"using":[9,20,91],"deep":[10,130],"learning":[11],"model":[12,131,158],"for":[13],"traffic":[14,37,64],"recognition":[15],"tasks,":[16],"a":[17,44],"method":[18,169,179],"Wasserstein":[21],"Generative":[22],"Adversarial":[23],"Network":[24],"(WGAN)":[25],"to":[26,42,51,79,119,144],"generate":[27],"minority":[28,107,175],"samples":[29],"based":[30],"on":[31,128],"image":[33],"original":[36,63],"packets":[39],"is":[40,70,88,159],"proposed":[41],"achieve":[43],"small":[45],"number":[46],"categories":[49],"expansion":[50],"solve":[52],"imbalance.":[57],"Firstly,":[58],"through":[59],"preprocessing,":[61],"PCAP":[65],"dataset":[69,87,123,127,153],"segmented,":[71],"filled,":[72],"and":[73,105,115,124,133,140,170],"mapped":[74],"into":[75],"grayscale":[76],"pictures":[77],"according":[78],"flow":[81],"unit.":[82],"Then,":[83],"balance":[85],"achieved":[89],"by":[90,155],"traditional":[92,167],"random":[93],"over":[94],"sampling,":[95],"WGAN":[96,157],"confrontation":[97],"network":[98],"generation":[99,103,165],"technology,":[100],"ordinary":[101,163],"GAN":[102,164],"technology":[104],"synthetic":[106],"oversampling":[108,168],"technology.":[109],"Finally,":[110],"public":[111],"datasets":[112],"USTC-":[113],"TFC2016":[114],"CICIDS2017":[116],"are":[117,142],"adopted":[118],"classify":[120],"unbalanced":[122],"balanced":[126,154],"classic":[129],"CNN,":[132],"three":[134],"evaluation":[135],"indicators":[136],"precision,":[138],"recall,":[139],"f1":[141],"applied":[143],"evaluate":[145],"classification":[146,183],"effect.":[147],"Experimental":[148],"results":[149],"show":[150],"that":[151],"better":[160],"than":[161],"method,":[166],"synthesis":[172],"class":[176],"sampling":[177],"technique":[178],"under":[180],"same":[182],"model.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-29T07:29:34.045763","created_date":"2025-10-10T00:00:00"}