{"id":"https://openalex.org/W4283837118","doi":"https://doi.org/10.1109/iwqos54832.2022.9812875","title":"AdvTraffic: Obfuscating Encrypted Traffic with Adversarial Examples","display_name":"AdvTraffic: Obfuscating Encrypted Traffic with Adversarial Examples","publication_year":2022,"publication_date":"2022-06-10","ids":{"openalex":"https://openalex.org/W4283837118","doi":"https://doi.org/10.1109/iwqos54832.2022.9812875"},"language":"en","primary_location":{"id":"doi:10.1109/iwqos54832.2022.9812875","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwqos54832.2022.9812875","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)","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/A5100458790","display_name":"Hao Liu","orcid":"https://orcid.org/0000-0002-1979-8994"},"institutions":[{"id":"https://openalex.org/I63135867","display_name":"University of Cincinnati","ror":"https://ror.org/01e3m7079","country_code":"US","type":"education","lineage":["https://openalex.org/I63135867"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hao Liu","raw_affiliation_strings":["University of Cincinnati"],"affiliations":[{"raw_affiliation_string":"University of Cincinnati","institution_ids":["https://openalex.org/I63135867"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070459927","display_name":"Jimmy Dani","orcid":"https://orcid.org/0000-0002-7581-9104"},"institutions":[{"id":"https://openalex.org/I63135867","display_name":"University of Cincinnati","ror":"https://ror.org/01e3m7079","country_code":"US","type":"education","lineage":["https://openalex.org/I63135867"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jimmy Dani","raw_affiliation_strings":["University of Cincinnati"],"affiliations":[{"raw_affiliation_string":"University of Cincinnati","institution_ids":["https://openalex.org/I63135867"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025512337","display_name":"Hongkai Yu","orcid":"https://orcid.org/0000-0001-5383-8913"},"institutions":[{"id":"https://openalex.org/I102607778","display_name":"Cleveland State University","ror":"https://ror.org/002tx1f22","country_code":"US","type":"education","lineage":["https://openalex.org/I102607778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongkai Yu","raw_affiliation_strings":["Cleveland State University"],"affiliations":[{"raw_affiliation_string":"Cleveland State University","institution_ids":["https://openalex.org/I102607778"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045191954","display_name":"Wenhai Sun","orcid":"https://orcid.org/0000-0003-0458-0092"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenhai Sun","raw_affiliation_strings":["Purdue University"],"affiliations":[{"raw_affiliation_string":"Purdue University","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100725035","display_name":"Boyang Wang","orcid":"https://orcid.org/0000-0001-8973-2328"},"institutions":[{"id":"https://openalex.org/I63135867","display_name":"University of Cincinnati","ror":"https://ror.org/01e3m7079","country_code":"US","type":"education","lineage":["https://openalex.org/I63135867"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Boyang Wang","raw_affiliation_strings":["University of Cincinnati"],"affiliations":[{"raw_affiliation_string":"University of Cincinnati","institution_ids":["https://openalex.org/I63135867"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100458790"],"corresponding_institution_ids":["https://openalex.org/I63135867"],"apc_list":null,"apc_paid":null,"fwci":1.1016,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.81116429,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"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":0.9998000264167786,"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":0.9998000264167786,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9976000189781189,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9911999702453613,"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/adversarial-system","display_name":"Adversarial system","score":0.8420008420944214},{"id":"https://openalex.org/keywords/encryption","display_name":"Encryption","score":0.7962316274642944},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7788374423980713},{"id":"https://openalex.org/keywords/obfuscation","display_name":"Obfuscation","score":0.7322394847869873},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.5931773781776428},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5715356469154358},{"id":"https://openalex.org/keywords/network-packet","display_name":"Network packet","score":0.5096845626831055},{"id":"https://openalex.org/keywords/traffic-analysis","display_name":"Traffic analysis","score":0.485434353351593},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.44384831190109253},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.42867010831832886},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31648993492126465}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8420008420944214},{"id":"https://openalex.org/C148730421","wikidata":"https://www.wikidata.org/wiki/Q141090","display_name":"Encryption","level":2,"score":0.7962316274642944},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7788374423980713},{"id":"https://openalex.org/C40305131","wikidata":"https://www.wikidata.org/wiki/Q2616305","display_name":"Obfuscation","level":2,"score":0.7322394847869873},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.5931773781776428},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5715356469154358},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.5096845626831055},{"id":"https://openalex.org/C2781317605","wikidata":"https://www.wikidata.org/wiki/Q7832483","display_name":"Traffic analysis","level":2,"score":0.485434353351593},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.44384831190109253},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.42867010831832886},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31648993492126465},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwqos54832.2022.9812875","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwqos54832.2022.9812875","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.6800000071525574,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"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":49,"referenced_works":["https://openalex.org/W9657784","https://openalex.org/W2021949962","https://openalex.org/W2027177092","https://openalex.org/W2135088779","https://openalex.org/W2135579486","https://openalex.org/W2243397390","https://openalex.org/W2272516773","https://openalex.org/W2331128040","https://openalex.org/W2485000773","https://openalex.org/W2490879758","https://openalex.org/W2557890466","https://openalex.org/W2603766943","https://openalex.org/W2620038827","https://openalex.org/W2739748921","https://openalex.org/W2752949934","https://openalex.org/W2783301790","https://openalex.org/W2944882402","https://openalex.org/W2946905885","https://openalex.org/W2963073614","https://openalex.org/W2963389226","https://openalex.org/W2963448658","https://openalex.org/W2963704216","https://openalex.org/W2963744840","https://openalex.org/W2964602598","https://openalex.org/W2969862110","https://openalex.org/W2989013751","https://openalex.org/W3031127859","https://openalex.org/W3047194494","https://openalex.org/W3049399107","https://openalex.org/W3103367901","https://openalex.org/W3103836116","https://openalex.org/W3107803931","https://openalex.org/W3135970545","https://openalex.org/W3155269739","https://openalex.org/W3157286348","https://openalex.org/W3211002588","https://openalex.org/W3216803405","https://openalex.org/W4224013247","https://openalex.org/W4293846201","https://openalex.org/W4294065829","https://openalex.org/W4320013936","https://openalex.org/W6693724512","https://openalex.org/W6729756640","https://openalex.org/W6730169306","https://openalex.org/W6739868092","https://openalex.org/W6741832134","https://openalex.org/W6744119509","https://openalex.org/W6781761031","https://openalex.org/W6794103382"],"related_works":["https://openalex.org/W4224216661","https://openalex.org/W2132360941","https://openalex.org/W3210347767","https://openalex.org/W2066014542","https://openalex.org/W2502115930","https://openalex.org/W67940504","https://openalex.org/W4290996806","https://openalex.org/W3166874438","https://openalex.org/W4289314221","https://openalex.org/W4206366499"],"abstract_inverted_index":{"Website":[0],"fingerprinting":[1,49,143],"can":[2,103,136],"reveal":[3],"which":[4,102],"sensitive":[5],"website":[6,30,48,142],"a":[7,24,68,97,153],"user":[8],"visits":[9],"over":[10,119],"encrypted":[11,15,73,120],"network":[12,91],"traffic.":[13,74,121,157],"Obfuscating":[14],"traffic,":[16],"e.g.,":[17],"adding":[18],"dummy":[19],"packets,":[20],"is":[21],"considered":[22],"as":[23,67],"primary":[25],"approach":[26],"to":[27,57,71,146,159],"defend":[28],"against":[29],"fingerprinting.":[31],"How-ever,":[32],"existing":[33,76,109],"defenses":[34],"relying":[35],"on":[36,53,113],"traffic":[37],"obfuscation":[38],"are":[39,80],"either":[40],"ineffective":[41],"or":[42],"introduce":[43],"significant":[44],"overheads.":[45],"As":[46],"recent":[47],"attacks":[50],"heavily":[51],"rely":[52],"deep":[54],"neural":[55],"networks":[56],"achieve":[58],"high":[59],"accuracy,":[60],"producing":[61],"adversarial":[62,77,110,117],"examples":[63,118],"could":[64],"be":[65],"utilized":[66],"new":[69,98],"way":[70],"obfuscate":[72],"Unfortunately,":[75],"example":[78,111],"algorithms":[79],"designed":[81],"for":[82,90],"images":[83,114],"and":[84,115,172],"do":[85],"not":[86],"consider":[87],"unique":[88],"challenges":[89],"traffic.In":[92],"this":[93],"paper,":[94],"we":[95],"design":[96],"method,":[99],"named":[100],"AdvTraffic,":[101,130],"customize":[104],"perturbations":[105],"produced":[106],"by":[107],"any":[108],"algorithm":[112],"derive":[116],"Our":[122],"experimental":[123],"results":[124],"show":[125],"that":[126],"the":[127,139,174],"integration":[128],"of":[129,141,166],"particularly":[131],"with":[132,155],"Generative":[133],"Adversarial":[134],"Networks,":[135],"effectively":[137],"mitigate":[138],"accuracy":[140,171],"from":[144],"95.0%":[145],"10.2%,":[147],"even":[148],"if":[149],"an":[150],"attacker":[151],"retrains":[152],"classifier":[154],"defended":[156],"Compared":[158],"other":[160],"defenses,":[161],"our":[162],"method":[163],"outperforms":[164],"most":[165],"them":[167],"in":[168],"mitigating":[169],"attack":[170],"offers":[173],"lowest":[175],"bandwidth":[176],"overhead.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}