{"id":"https://openalex.org/W4402158950","doi":"https://doi.org/10.1109/icc51166.2024.10623093","title":"Network Traffic Classification with Small-Scale Datasets Using Ensemble Learning","display_name":"Network Traffic Classification with Small-Scale Datasets Using Ensemble Learning","publication_year":2024,"publication_date":"2024-06-09","ids":{"openalex":"https://openalex.org/W4402158950","doi":"https://doi.org/10.1109/icc51166.2024.10623093"},"language":"en","primary_location":{"id":"doi:10.1109/icc51166.2024.10623093","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icc51166.2024.10623093","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2024 - IEEE International Conference on Communications","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/A5101652531","display_name":"Xiaorong Wang","orcid":"https://orcid.org/0000-0001-7065-8615"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaorong Wang","raw_affiliation_strings":["State Key Laboratory of Integrated Service Networks, Xidian University,Xi&#x0027;an,China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Networks, Xidian University,Xi&#x0027;an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083177219","display_name":"Wenting Wei","orcid":"https://orcid.org/0000-0003-1795-4938"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenting Wei","raw_affiliation_strings":["State Key Laboratory of Integrated Service Networks, Xidian University,Xi&#x0027;an,China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Networks, Xidian University,Xi&#x0027;an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100731338","display_name":"Chenyu Liu","orcid":"https://orcid.org/0000-0002-7611-1490"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenyu Liu","raw_affiliation_strings":["State Key Laboratory of Integrated Service Networks, Xidian University,Xi&#x0027;an,China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Networks, Xidian University,Xi&#x0027;an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072559250","display_name":"Xindan Zhang","orcid":"https://orcid.org/0009-0003-1716-5995"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xindan Zhang","raw_affiliation_strings":["State Key Laboratory of Integrated Service Networks, Xidian University,Xi&#x0027;an,China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Networks, Xidian University,Xi&#x0027;an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090956901","display_name":"Weicheng Lu","orcid":"https://orcid.org/0000-0001-6498-0931"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weicheng Lu","raw_affiliation_strings":["State Key Laboratory of Integrated Service Networks, Xidian University,Xi&#x0027;an,China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Networks, Xidian University,Xi&#x0027;an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006869296","display_name":"Yihan Zhong","orcid":"https://orcid.org/0000-0002-1462-3642"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yihan Zhong","raw_affiliation_strings":["Georgia State University,Department of Computer Science,USA"],"affiliations":[{"raw_affiliation_string":"Georgia State University,Department of Computer Science,USA","institution_ids":["https://openalex.org/I181565077"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101652531"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.3862,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.66558006,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"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":1.0,"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":1.0,"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":0.9997000098228455,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.7298450469970703},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5857976078987122},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5129756331443787},{"id":"https://openalex.org/keywords/traffic-classification","display_name":"Traffic classification","score":0.46382418274879456},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4354534447193146},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.42396965622901917},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33740371465682983},{"id":"https://openalex.org/keywords/the-internet","display_name":"The Internet","score":0.08667278289794922},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.06605866551399231},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.053919076919555664}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7298450469970703},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5857976078987122},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5129756331443787},{"id":"https://openalex.org/C169988225","wikidata":"https://www.wikidata.org/wiki/Q7832484","display_name":"Traffic classification","level":3,"score":0.46382418274879456},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4354534447193146},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.42396965622901917},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33740371465682983},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.08667278289794922},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.06605866551399231},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.053919076919555664},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc51166.2024.10623093","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icc51166.2024.10623093","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2024 - IEEE International Conference on Communications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6485525536","display_name":null,"funder_award_id":"62102302","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W146900863","https://openalex.org/W1521553548","https://openalex.org/W2012095206","https://openalex.org/W2096118443","https://openalex.org/W2168686833","https://openalex.org/W2171634548","https://openalex.org/W2343828539","https://openalex.org/W2591712613","https://openalex.org/W2743678626","https://openalex.org/W2750674396","https://openalex.org/W2897202622","https://openalex.org/W2919493784","https://openalex.org/W3163279171","https://openalex.org/W3213120752","https://openalex.org/W4206337041","https://openalex.org/W4220863835","https://openalex.org/W4296473472","https://openalex.org/W4320015763","https://openalex.org/W4361984382","https://openalex.org/W4367665491","https://openalex.org/W4386558805","https://openalex.org/W6739901393"],"related_works":["https://openalex.org/W4376643315","https://openalex.org/W4324137541","https://openalex.org/W2900445707","https://openalex.org/W4285741730","https://openalex.org/W1191482210","https://openalex.org/W4285046548","https://openalex.org/W4210302090","https://openalex.org/W4401008042","https://openalex.org/W4375951447","https://openalex.org/W2159958314"],"abstract_inverted_index":{"Traffic":[0],"classification":[1,23,83,171,177],"is":[2,61],"a":[3,25,79,112,147,160,170,176],"fundamental":[4],"tool":[5],"for":[6],"network":[7],"management,":[8],"measurement":[9],"and":[10,56,66,123,175],"security.":[11],"As":[12],"the":[13,70,98,102,133],"new":[14],"services":[15],"with":[16,146,159],"diversified":[17],"QoS":[18],"requirements":[19],"are":[20,95],"evolving,":[21],"traffic":[22,43,82],"plays":[24],"more":[26,121],"significant":[27],"role":[28],"in":[29,69],"ensuring":[30],"end-to-end":[31],"performance":[32],"guarantees.":[33],"Several":[34],"efforts":[35],"have":[36],"introduced":[37],"deep":[38],"learning":[39,110,125],"(DL)":[40],"to":[41,63,100,119],"train":[42,101],"classifiers":[44,50,137],"without":[45],"manual":[46],"features,":[47],"however,":[48],"these":[49],"depend":[51],"heavily":[52],"on":[53,138],"numerous":[54],"samples":[55,68],"their":[57],"quality.":[58],"Indeed,":[59],"it":[60],"unrealistic":[62],"obtain":[64],"sufficient":[65],"representative":[67],"underlying":[71],"real":[72],"network.":[73],"In":[74],"this":[75],"paper,":[76],"we":[77],"propose":[78],"highly":[80],"accurate":[81,122],"model":[84,168],"by":[85],"an":[86],"Ensemble":[87],"Learning":[88],"framework,":[89],"where":[90],"Convolutional":[91],"Recurrent":[92],"Neural":[93],"Networks":[94],"integrated":[96],"into":[97],"Bagging":[99],"classifier":[103],"using":[104],"only":[105],"small-scale":[106],"datasets.":[107],"Specifically,":[108],"ensemble":[109],"employs":[111],"combinatorial":[113],"design":[114],"of":[115,135,163,173,179],"three":[116],"base":[117],"models":[118,126],"build":[120],"robust":[124],"so":[127],"that":[128,157],"absorbing":[129],"features":[130],"opens":[131],"up":[132],"possibility":[134],"training":[136,164],"small":[139],"sample":[140],"sets.":[141],"We":[142],"conduct":[143],"comprehensive":[144],"experiments":[145],"real-world":[148],"dataset":[149],"encompassing":[150],"20":[151],"applications.":[152],"Extensive":[153],"experiment":[154],"results":[155],"demonstrate":[156],"even":[158],"mere":[161],"10%":[162],"samples,":[165],"our":[166],"proposed":[167],"attains":[169],"accuracy":[172],"93.94%":[174],"precision":[178],"94.33%,":[180],"outperforming":[181],"multiple":[182],"other":[183],"cutting-edge":[184],"methods.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}
