{"id":"https://openalex.org/W7131086850","doi":"https://doi.org/10.1109/access.2026.3667347","title":"A Framework for Encrypted Traffic Classification With Decoupled Yet Aligned Information Representation","display_name":"A Framework for Encrypted Traffic Classification With Decoupled Yet Aligned Information Representation","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7131086850","doi":"https://doi.org/10.1109/access.2026.3667347"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3667347","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3667347","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3667347","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126599815","display_name":"Wei Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210090490","display_name":"National University","ror":"https://ror.org/000a8qk84","country_code":"PH","type":"education","lineage":["https://openalex.org/I4210090490"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Wei Lin","raw_affiliation_strings":["College of Computing and Information Technologies, National University, Manila, Philippines"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computing and Information Technologies, National University, Manila, Philippines","institution_ids":["https://openalex.org/I4210090490"]}]},{"author_position":"last","author":{"id":null,"display_name":"Eric B. Blancaflor","orcid":"https://orcid.org/0000-0002-7189-3040"},"institutions":[{"id":"https://openalex.org/I5791819","display_name":"University of the Philippines Manila","ror":"https://ror.org/01rrczv41","country_code":"PH","type":"education","lineage":["https://openalex.org/I103911934","https://openalex.org/I5791819"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Eric B. Blancaflor","raw_affiliation_strings":["School of Information Technology, Mapúa University, Manila, Philippines"],"raw_orcid":"https://orcid.org/0000-0002-7189-3040","affiliations":[{"raw_affiliation_string":"School of Information Technology, Mapúa University, Manila, Philippines","institution_ids":["https://openalex.org/I5791819"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17911881,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"33046","last_page":"33056"},"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":0.9718000292778015,"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":0.9718000292778015,"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":0.007000000216066837,"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/T12326","display_name":"Network Packet Processing and Optimization","score":0.0024999999441206455,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/concatenation","display_name":"Concatenation (mathematics)","score":0.6230000257492065},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.48080000281333923},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4489000141620636},{"id":"https://openalex.org/keywords/complementarity","display_name":"Complementarity (molecular biology)","score":0.44600000977516174},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.43160000443458557},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.41780000925064087},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41749998927116394},{"id":"https://openalex.org/keywords/disjoint-sets","display_name":"Disjoint sets","score":0.41040000319480896},{"id":"https://openalex.org/keywords/encryption","display_name":"Encryption","score":0.3919000029563904}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7950999736785889},{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.6230000257492065},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5112000107765198},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5004000067710876},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.48080000281333923},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4489000141620636},{"id":"https://openalex.org/C202269582","wikidata":"https://www.wikidata.org/wiki/Q2644277","display_name":"Complementarity (molecular biology)","level":2,"score":0.44600000977516174},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.41780000925064087},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41749998927116394},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.41040000319480896},{"id":"https://openalex.org/C148730421","wikidata":"https://www.wikidata.org/wiki/Q141090","display_name":"Encryption","level":2,"score":0.3919000029563904},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.37869998812675476},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37599998712539673},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3723999857902527},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.36320000886917114},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.36079999804496765},{"id":"https://openalex.org/C169988225","wikidata":"https://www.wikidata.org/wiki/Q7832484","display_name":"Traffic classification","level":3,"score":0.34610000252723694},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.3449999988079071},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3411000072956085},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.33500000834465027},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C99221444","wikidata":"https://www.wikidata.org/wiki/Q1532069","display_name":"Private information retrieval","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C56086750","wikidata":"https://www.wikidata.org/wiki/Q6042592","display_name":"Integer programming","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.26919999718666077},{"id":"https://openalex.org/C541664917","wikidata":"https://www.wikidata.org/wiki/Q14001","display_name":"Malware","level":2,"score":0.2583000063896179}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3667347","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3667347","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:16b67f95796246d2bc2453f396c4268f","is_oa":true,"landing_page_url":"https://doaj.org/article/16b67f95796246d2bc2453f396c4268f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 33046-33056 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3667347","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3667347","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"extensive":[1],"deployment":[2],"of":[3,16,27,99,136,150,164,195,238],"end-to-end":[4],"encryption":[5],"renders":[6],"traditional":[7],"network":[8],"traffic":[9],"analysis":[10],"methods":[11],"ineffective,":[12],"necessitating":[13],"the":[14,96,140,146,151,196,201,222],"development":[15],"new":[17,81],"advanced":[18],"deep":[19],"models":[20,26],"with":[21],"high":[22],"classification":[23,189,239],"performance.":[24,75],"Combination":[25],"CNNs":[28],"and":[29,38,73,103,112,177,184,200,213,226],"Transformers":[30],"have":[31],"shown":[32],"efficacy":[33],"by":[34,94,231],"acquiring":[35],"both":[36],"local":[37],"global":[39],"feature":[40,53,58,97,186],"extraction":[41],"capabilities.":[42],"Existing":[43],"hybrid":[44],"architectures":[45],"often":[46],"adopt":[47],"heuristic":[48],"fusion":[49],"operations":[50],"(e.g.,":[51],"direct":[52],"concatenation":[54],"or":[55],"hand-crafted":[56],"cross-branch":[57],"exchange),":[59],"without":[60],"explicitly":[61],"encouraging":[62],"complementarity":[63],"between":[64],"heterogeneous":[65],"representations,":[66],"which":[67],"may":[68],"result":[69],"in":[70,89],"redundant":[71],"features":[72,199],"suboptimal":[74],"This":[76,175],"paper":[77],"introduces":[78],"DAIR-MTC,":[79],"a":[80,109,113,118,127,154,181,232,236],"model":[82,225],"for":[83],"Decoupled":[84],"but":[85],"Aligned":[86],"Information":[87],"Representation":[88],"Multi-Task":[90],"Classification.":[91],"DAIR-MTC":[92,218],"innovates":[93],"splitting":[95],"space":[98,111],"each":[100,168],"branch":[101,169],"(1D-CNN":[102],"Transformer)":[104],"into":[105],"two":[106,141],"disjoint":[107],"subspaces:":[108],"shared":[110,137],"private":[114,165,203],"space.":[115],"We":[116],"introduce":[117],"dual":[119],"information-theoretic":[120],"goal":[121],"to":[122,133,143,161,170],"structure":[123],"this":[124],"representation.":[125,187],"First,":[126],"contrastive":[128],"learning":[129],"loss":[130,158],"is":[131,159,190],"employed":[132,160],"enforce":[134,162],"alignment":[135],"features,":[138,166],"compelling":[139,167],"branches":[142],"agree":[144],"on":[145,192,206,209,235],"intrinsic,":[147],"modality-invariant":[148],"characteristics":[149],"traffic.":[152],"Second,":[153],"mutual":[155],"information":[156],"minimization":[157],"decoupling":[163],"extract":[171],"distinctive,":[172],"complementary":[173],"information.":[174],"\u2018\u2018consensus":[176],"specificity\u2019\u2019":[178],"framework":[179],"creates":[180],"highly":[182],"efficient":[183],"robust":[185],"Final":[188],"done":[191],"an":[193],"aggregation":[194],"aligned":[197],"common":[198],"diversified":[202],"features.":[204],"Experiments":[205],"large":[207],"scales":[208],"benchmark":[210],"ISCX":[211],"VPN-nonVPN":[212],"CICIDS2017":[214],"datasets":[215],"indicate":[216],"that":[217],"performs":[219],"better":[220],"than":[221],"baseline":[223],"MTC":[224],"numerous":[227],"other":[228],"state-of-the-art":[229],"approaches":[230],"significant":[233],"margin":[234],"range":[237],"tasks.":[240]},"counts_by_year":[],"updated_date":"2026-03-06T06:45:51.903784","created_date":"2026-02-24T00:00:00"}