{"id":"https://openalex.org/W3048465885","doi":"https://doi.org/10.1109/infocomwkshps50562.2020.9162940","title":"Detecting Anomalies in Encrypted Traffic via Deep Dictionary Learning","display_name":"Detecting Anomalies in Encrypted Traffic via Deep Dictionary Learning","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3048465885","doi":"https://doi.org/10.1109/infocomwkshps50562.2020.9162940","mag":"3048465885"},"language":"en","primary_location":{"id":"doi:10.1109/infocomwkshps50562.2020.9162940","is_oa":false,"landing_page_url":"https://doi.org/10.1109/infocomwkshps50562.2020.9162940","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","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/A5049670126","display_name":"Junchi Xing","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Junchi Xing","raw_affiliation_strings":["College of Computer Science and Technology, m Zhejiang University"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, m Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078198240","display_name":"Chunming Wu","orcid":"https://orcid.org/0000-0001-7958-9687"},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunming Wu","raw_affiliation_strings":["Zhejiang Lab"],"affiliations":[{"raw_affiliation_string":"Zhejiang Lab","institution_ids":["https://openalex.org/I4210123185"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5049670126"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":1.6465,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.87343574,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"734","last_page":"739"},"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":0.9998999834060669,"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":0.9998999834060669,"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":0.9998000264167786,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994000196456909,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8365240693092346},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8125787973403931},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7512022852897644},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6801846623420715},{"id":"https://openalex.org/keywords/encryption","display_name":"Encryption","score":0.6609376668930054},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6284114122390747},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.5219413638114929},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49589213728904724},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4760211110115051},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.43757331371307373},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.41625431180000305},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3879971504211426},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3184102475643158}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8365240693092346},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8125787973403931},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7512022852897644},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6801846623420715},{"id":"https://openalex.org/C148730421","wikidata":"https://www.wikidata.org/wiki/Q141090","display_name":"Encryption","level":2,"score":0.6609376668930054},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6284114122390747},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5219413638114929},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49589213728904724},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4760211110115051},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.43757331371307373},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.41625431180000305},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3879971504211426},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3184102475643158},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/infocomwkshps50562.2020.9162940","is_oa":false,"landing_page_url":"https://doi.org/10.1109/infocomwkshps50562.2020.9162940","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.5}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W70584117","https://openalex.org/W2031163547","https://openalex.org/W2052794430","https://openalex.org/W2064675550","https://openalex.org/W2107055466","https://openalex.org/W2115706991","https://openalex.org/W2157331557","https://openalex.org/W2164210932","https://openalex.org/W2254140767","https://openalex.org/W2480454766","https://openalex.org/W2537766808","https://openalex.org/W2565575913","https://openalex.org/W2743556905","https://openalex.org/W2798381345","https://openalex.org/W2912711574","https://openalex.org/W2919493784","https://openalex.org/W2959716986","https://openalex.org/W2962709635","https://openalex.org/W2963065250","https://openalex.org/W2963467671","https://openalex.org/W2963516518","https://openalex.org/W2965828587","https://openalex.org/W2996328026","https://openalex.org/W4301866626","https://openalex.org/W6602816918","https://openalex.org/W6638896900","https://openalex.org/W6676006193","https://openalex.org/W6691677089","https://openalex.org/W6744538726"],"related_works":["https://openalex.org/W2159052453","https://openalex.org/W3013693939","https://openalex.org/W3186512740","https://openalex.org/W3017266184","https://openalex.org/W2918377632","https://openalex.org/W3202913553","https://openalex.org/W3046391934","https://openalex.org/W3194885736","https://openalex.org/W4363671829","https://openalex.org/W4292947472"],"abstract_inverted_index":{"The":[0,140],"widely":[1],"used":[2],"encryption":[3],"of":[4,24,27,81,112,127],"network":[5],"traffic":[6,30,57,76],"poses":[7],"a":[8,125],"great":[9],"challenge":[10],"to":[11,69,87,94,119],"anomaly":[12,52,110],"detection.":[13],"Currently,":[14],"the":[15,22,67,79,88,96,109,120,156],"supervised":[16],"and":[17,32,50,129,133,150,154],"semi-supervised":[18],"solutions":[19],"suffer":[20],"from":[21,73],"problems":[23],"noisy":[25],"label":[26],"data,":[28],"non-stationary":[29],"distribution,":[31],"huge":[33],"resource":[34,152],"consumption":[35],"for":[36,55],"offline":[37],"training.":[38],"To":[39],"address":[40],"this,":[41],"in":[42,160],"this":[43],"paper,":[44],"we":[45],"present":[46],"an":[47,82],"unsupervised,":[48],"robust,":[49],"online":[51],"detection":[53],"method":[54,146],"encrypted":[56,75],"by":[58,100,115,135],"using":[59,137],"deep":[60,102,121],"dictionary":[61,103],"learning,":[62],"called":[63],"D2LAD.":[64],"D2LAD":[65,91,106,128],"offers":[66],"potential":[68],"extract":[70],"sequential":[71,89],"features":[72],"raw":[74,113],"data":[77,114],"with":[78],"help":[80],"LSTM-based":[83],"autoencoder.":[84],"Then,":[85],"according":[86],"features,":[90],"is":[92],"able":[93],"explore":[95],"hidden":[97],"normal":[98],"patterns":[99],"iterative":[101],"learning.":[104],"Eventually,":[105],"can":[107],"obtain":[108],"score":[111],"calculating":[116],"its":[117,131],"relevance":[118],"dictionary.":[122],"We":[123],"implement":[124],"prototype":[126],"evaluate":[130],"effectiveness":[132],"performance":[134],"experiments":[136],"realistic":[138],"datasets.":[139],"experimental":[141],"results":[142],"show":[143],"that":[144],"our":[145],"achieves":[147],"high":[148],"accuracy":[149],"low":[151],"usage,":[153],"outperforms":[155],"representative":[157],"state-of-the-art":[158],"methods":[159],"real-world":[161],"settings.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":2}],"updated_date":"2026-03-15T09:29:46.208133","created_date":"2025-10-10T00:00:00"}
