{"id":"https://openalex.org/W2939074566","doi":"https://doi.org/10.1145/3314545.3316278","title":"Effective Media Traffic Classification Using Deep Learning","display_name":"Effective Media Traffic Classification Using Deep Learning","publication_year":2019,"publication_date":"2019-03-14","ids":{"openalex":"https://openalex.org/W2939074566","doi":"https://doi.org/10.1145/3314545.3316278","mag":"2939074566"},"language":"en","primary_location":{"id":"doi:10.1145/3314545.3316278","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3314545.3316278","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Compute and Data Analysis","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/A5057851690","display_name":"Qing Lyu","orcid":"https://orcid.org/0000-0002-9824-0170"},"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":true,"raw_author_name":"Qing Lyu","raw_affiliation_strings":["Tsinghua University, Beijing"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101773230","display_name":"Xingjian Lu","orcid":"https://orcid.org/0000-0002-6935-5614"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingjian Lu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications Beijing"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications Beijing","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5057851690"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":1.2601,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.8480888,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"139","last_page":"146"},"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":0.9965999722480774,"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/T10388","display_name":"Advanced Steganography and Watermarking Techniques","score":0.9886000156402588,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.8334126472473145},{"id":"https://openalex.org/keywords/traffic-classification","display_name":"Traffic classification","score":0.8138141632080078},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7069629430770874},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6424631476402283},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5399899482727051},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.5099886655807495},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5061201453208923},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4950954020023346},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40168002247810364},{"id":"https://openalex.org/keywords/the-internet","display_name":"The Internet","score":0.1395745873451233},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.08636215329170227}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8334126472473145},{"id":"https://openalex.org/C169988225","wikidata":"https://www.wikidata.org/wiki/Q7832484","display_name":"Traffic classification","level":3,"score":0.8138141632080078},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7069629430770874},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6424631476402283},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5399899482727051},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.5099886655807495},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5061201453208923},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4950954020023346},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40168002247810364},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.1395745873451233},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.08636215329170227}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3314545.3316278","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3314545.3316278","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Compute and Data Analysis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320322392","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W88694106","https://openalex.org/W1019830208","https://openalex.org/W1495633614","https://openalex.org/W1498436455","https://openalex.org/W1521553548","https://openalex.org/W1547382240","https://openalex.org/W1686810756","https://openalex.org/W1971622065","https://openalex.org/W2012095206","https://openalex.org/W2046327340","https://openalex.org/W2056625091","https://openalex.org/W2062401262","https://openalex.org/W2076448839","https://openalex.org/W2089194229","https://openalex.org/W2101222264","https://openalex.org/W2107528096","https://openalex.org/W2112796928","https://openalex.org/W2124863430","https://openalex.org/W2130325614","https://openalex.org/W2132083787","https://openalex.org/W2133665405","https://openalex.org/W2137831706","https://openalex.org/W2147118406","https://openalex.org/W2149600645","https://openalex.org/W2157733597","https://openalex.org/W2242711166","https://openalex.org/W2252215182","https://openalex.org/W2256352919","https://openalex.org/W2301676159","https://openalex.org/W2315104825","https://openalex.org/W2511505324","https://openalex.org/W2588781394","https://openalex.org/W2740547213","https://openalex.org/W2753434589","https://openalex.org/W2767675758","https://openalex.org/W2800828017","https://openalex.org/W2883430943","https://openalex.org/W2892460540","https://openalex.org/W2895715183","https://openalex.org/W2901366940","https://openalex.org/W3146803896","https://openalex.org/W4232013398","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W3158157485","https://openalex.org/W2789124470"],"abstract_inverted_index":{"Traffic":[0],"classification":[1,52,85],"(TC)":[2],"is":[3],"very":[4,103],"important":[5],"as":[6],"it":[7],"can":[8,13,112],"provide":[9],"useful":[10],"information":[11],"which":[12],"be":[14],"used":[15],"in":[16,106,121],"the":[17,21,33,51,71,92,114,118,122],"flexible":[18],"management":[19],"of":[20,32,35,53,57,117],"network.":[22],"However,":[23],"TC":[24],"has":[25,102],"become":[26],"more":[27,29],"and":[28,39,64,78],"complicated":[30],"because":[31],"emergence":[34],"various":[36],"network":[37,73,120],"applications":[38],"techniques.":[40],"In":[41],"this":[42],"paper,":[43],"we":[44],"apply":[45],"deep":[46],"learning":[47],"based":[48,83],"method":[49],"to":[50,89],"four":[54],"different":[55,96],"kinds":[56],"media":[58],"traffic,":[59],"i.e.,":[60],"audio,":[61],"picture,":[62],"text":[63],"video.":[65],"We":[66,98],"collect":[67],"traffic":[68,84,94],"data":[69],"from":[70],"real":[72],"environment.":[74],"Multilayer":[75],"Perceptron":[76],"(MLP)":[77],"Convolutional":[79],"Neural":[80],"Network":[81],"(CNN)":[82],"methods":[86],"are":[87],"designed":[88],"accurately":[90],"classify":[91],"target":[93],"into":[95],"categories.":[97],"found":[99],"that":[100],"MLP":[101],"good":[104],"performance":[105],"most":[107],"scenarios.":[108],"Moreover,":[109],"specific":[110],"architecture":[111],"reduce":[113],"training":[115],"time":[116],"neural":[119],"classification.":[123]},"counts_by_year":[{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
