{"id":"https://openalex.org/W4416549552","doi":"https://doi.org/10.1145/3719027.3765073","title":"Training Robust Classifiers for Classifying Encrypted Traffic under Dynamic Network Conditions","display_name":"Training Robust Classifiers for Classifying Encrypted Traffic under Dynamic Network Conditions","publication_year":2025,"publication_date":"2025-11-19","ids":{"openalex":"https://openalex.org/W4416549552","doi":"https://doi.org/10.1145/3719027.3765073"},"language":null,"primary_location":{"id":"doi:10.1145/3719027.3765073","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3719027.3765073","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3719027.3765073","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3719027.3765073","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112999768","display_name":"Yuqi Qing","orcid":"https://orcid.org/0009-0005-4226-9387"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuqi Qing","raw_affiliation_strings":["INSC, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-4226-9387","affiliations":[{"raw_affiliation_string":"INSC, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001953231","display_name":"Qilei Yin","orcid":"https://orcid.org/0000-0002-0148-2772"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qilei Yin","raw_affiliation_strings":["Zhongguancun Laboratory, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0148-2772","affiliations":[{"raw_affiliation_string":"Zhongguancun Laboratory, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068492222","display_name":"Xinhao Deng","orcid":"https://orcid.org/0000-0002-4366-4777"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinhao Deng","raw_affiliation_strings":["INSC, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4366-4777","affiliations":[{"raw_affiliation_string":"INSC, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115602529","display_name":"Xiaoli Zhang","orcid":"https://orcid.org/0009-0005-8317-5539"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoli Zhang","raw_affiliation_strings":["University of Science and Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-8317-5539","affiliations":[{"raw_affiliation_string":"University of Science and Technology, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068031202","display_name":"Peiyang Li","orcid":"https://orcid.org/0009-0007-8663-2103"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peiyang Li","raw_affiliation_strings":["INSC, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-8663-2103","affiliations":[{"raw_affiliation_string":"INSC, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045206037","display_name":"Zhuotao Liu","orcid":"https://orcid.org/0000-0002-7532-0434"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuotao Liu","raw_affiliation_strings":["INSC, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7532-0434","affiliations":[{"raw_affiliation_string":"INSC, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026728546","display_name":"Kun Sun","orcid":"https://orcid.org/0000-0003-4152-2107"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kun Sun","raw_affiliation_strings":["IST, George Mason University, Fairfax, Virginia, USA"],"raw_orcid":"https://orcid.org/0000-0003-4152-2107","affiliations":[{"raw_affiliation_string":"IST, George Mason University, Fairfax, Virginia, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100665814","display_name":"Ke Xu","orcid":"https://orcid.org/0000-0003-2587-8517"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Xu","raw_affiliation_strings":["DCST, Tsinghua University, Beijing, China and Zhongguancun Laboratory, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2587-8517","affiliations":[{"raw_affiliation_string":"DCST, Tsinghua University, Beijing, China and Zhongguancun Laboratory, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100350165","display_name":"Qi Li","orcid":"https://orcid.org/0000-0001-8776-8730"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Li","raw_affiliation_strings":["INSC, Tsinghua University, Beijing, China and Zhongguancun Laboratory, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8776-8730","affiliations":[{"raw_affiliation_string":"INSC, Tsinghua University, Beijing, China and Zhongguancun Laboratory, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.673,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.87678647,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3564","last_page":"3578"},"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":0.9922999739646912,"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":0.9922999739646912,"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":0.0010999999940395355,"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/T14258","display_name":"Legal and Policy Issues","score":0.00039999998989515007,"subfield":{"id":"https://openalex.org/subfields/3308","display_name":"Law"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/encryption","display_name":"Encryption","score":0.6919000148773193},{"id":"https://openalex.org/keywords/traffic-classification","display_name":"Traffic classification","score":0.6471999883651733},{"id":"https://openalex.org/keywords/dynamic-network-analysis","display_name":"Dynamic network analysis","score":0.5720000267028809},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5669999718666077},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.565500020980835},{"id":"https://openalex.org/keywords/cover","display_name":"Cover (algebra)","score":0.39320001006126404},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.38659998774528503},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.35499998927116394}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.703499972820282},{"id":"https://openalex.org/C148730421","wikidata":"https://www.wikidata.org/wiki/Q141090","display_name":"Encryption","level":2,"score":0.6919000148773193},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6571000218391418},{"id":"https://openalex.org/C169988225","wikidata":"https://www.wikidata.org/wiki/Q7832484","display_name":"Traffic classification","level":3,"score":0.6471999883651733},{"id":"https://openalex.org/C13540734","wikidata":"https://www.wikidata.org/wiki/Q5318996","display_name":"Dynamic network analysis","level":2,"score":0.5720000267028809},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5669999718666077},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.565500020980835},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4837000072002411},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47690001130104065},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.39320001006126404},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.38659998774528503},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35499998927116394},{"id":"https://openalex.org/C182590292","wikidata":"https://www.wikidata.org/wiki/Q989632","display_name":"Network security","level":2,"score":0.33889999985694885},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C203274722","wikidata":"https://www.wikidata.org/wiki/Q7001161","display_name":"Network performance","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.27889999747276306},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26080000400543213}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3719027.3765073","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3719027.3765073","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3719027.3765073","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3719027.3765073","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3719027.3765073","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3719027.3765073","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3562635767","display_name":null,"funder_award_id":"62472247","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5252299824","display_name":null,"funder_award_id":"62425201","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5831147741","display_name":null,"funder_award_id":"62302452","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7155321176","display_name":null,"funder_award_id":"62132011","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/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416549552.pdf","grobid_xml":"https://content.openalex.org/works/W4416549552.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W2012210084","https://openalex.org/W2021949962","https://openalex.org/W2027177092","https://openalex.org/W2129141174","https://openalex.org/W2135579486","https://openalex.org/W2485000773","https://openalex.org/W2537766808","https://openalex.org/W2743556905","https://openalex.org/W2743678626","https://openalex.org/W2744198018","https://openalex.org/W2919493784","https://openalex.org/W2963043696","https://openalex.org/W2963522561","https://openalex.org/W2963704216","https://openalex.org/W2964057616","https://openalex.org/W2967717386","https://openalex.org/W3012902672","https://openalex.org/W3035654071","https://openalex.org/W3095746859","https://openalex.org/W3177379791","https://openalex.org/W3179233662","https://openalex.org/W3198759872","https://openalex.org/W3205249428","https://openalex.org/W4214611271","https://openalex.org/W4306406279","https://openalex.org/W4367047070","https://openalex.org/W4383108934","https://openalex.org/W4385577745","https://openalex.org/W4386694208","https://openalex.org/W4390190009","https://openalex.org/W4393148824","https://openalex.org/W4396758681","https://openalex.org/W4403577465","https://openalex.org/W4405183144","https://openalex.org/W4407782069"],"related_works":[],"abstract_inverted_index":{"Most":[0],"existing":[1,201],"DL-based":[2],"encrypted":[3,29,68,163],"traffic":[4,23,30,69,81,123,164],"classification":[5,70,132],"methods":[6,204],"suffer":[7],"performance":[8,61],"degradation":[9],"in":[10,118],"real-world":[11],"deployments":[12],"due":[13],"to":[14,31,148],"dynamic":[15,72,134,178],"network":[16,19,26,73,85,95,127,135,171,182],"conditions,":[17,96,128,183],"e.g.,":[18],"environment":[20],"changes":[21],"and":[22,38,50,159,194,210],"obfuscation.":[24],"Dynamic":[25],"conditions":[27],"cause":[28],"exhibit":[32],"distinct":[33],"feature":[34,103,112],"patterns":[35],"during":[36],"training":[37,53,203],"testing":[39],"phases.":[40],"To":[41],"address":[42],"this":[43],"issue,":[44],"we":[45],"propose":[46],"MetaTraffic,":[47],"a":[48],"novel":[49],"general":[51],"DL":[52,64,108,190],"framework":[54,185],"built":[55],"upon":[56],"meta-learning":[57,142],"that":[58,79,166],"enhances":[59],"the":[60,80,83,88,116,120,187,195,206,211],"of":[62,82,170,181,189],"supervised":[63],"models":[65,109,121,191],"designed":[66],"for":[67],"against":[71],"conditions.":[74,136,172],"Our":[75],"key":[76],"observation":[77],"is":[78],"same":[84,89],"behaviors":[86],"share":[87],"semantic":[90],"features":[91,124],"even":[92],"under":[93,125,133,177],"different":[94,126],"which":[97],"can":[98],"be":[99],"considered":[100],"as":[101],"stable":[102,111],"representations.":[104],"Therefore,":[105],"MetaTraffic":[106,139,154],"helps":[107],"learn":[110],"representations":[113],"by":[114,192,198,208,214],"minimizing":[115],"discrepancies":[117],"how":[119],"represent":[122],"thereby":[129],"achieving":[130],"robust":[131,202],"We":[137,152],"implement":[138],"based":[140],"on":[141],"with":[143],"three":[144,156,160],"innovative":[145],"facilitate":[146],"modules":[147],"enhance":[149],"its":[150],"performance.":[151],"evaluate":[153],"using":[155],"public":[157],"datasets":[158,165],"new":[161],"large-scale":[162],"cover":[167],"multiple":[168,179],"types":[169,180],"Experimental":[173],"results":[174],"show":[175],"that,":[176],"our":[184],"improves":[186],"accuracy":[188,207],"8.94%":[193],"F1-Macro":[196,212],"score":[197,213],"12.55%,":[199],"while":[200],"decrease":[205],"28.85%":[209],"33.52%.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-11-23T00:00:00"}
