{"id":"https://openalex.org/W2028299842","doi":"https://doi.org/10.1109/icnsc.2008.4525474","title":"Efficient, Accurate Internet Traffic Classification using Discretization in Naive Bayes","display_name":"Efficient, Accurate Internet Traffic Classification using Discretization in Naive Bayes","publication_year":2008,"publication_date":"2008-04-01","ids":{"openalex":"https://openalex.org/W2028299842","doi":"https://doi.org/10.1109/icnsc.2008.4525474","mag":"2028299842"},"language":"en","primary_location":{"id":"doi:10.1109/icnsc.2008.4525474","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnsc.2008.4525474","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Conference on Networking, Sensing and Control","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/A5014505438","display_name":"Yuhai Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210161003","display_name":"Qingdao Center of Resource Chemistry and New Materials","ror":"https://ror.org/05x85k702","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210089297","https://openalex.org/I4210161003"]},{"id":"https://openalex.org/I1322087612","display_name":"Alcatel Lucent (Germany)","ror":"https://ror.org/00c5mwp75","country_code":"DE","type":"company","lineage":["https://openalex.org/I1322087612"]}],"countries":["CN","DE"],"is_corresponding":true,"raw_author_name":"Yuhai Liu","raw_affiliation_strings":["Plexus Team Qingdao Research and Development Center, Alcatel-Lucent Technologies, Inc., Qingdao, China","Alcatel-Lucent Technol., Murray Hill"],"affiliations":[{"raw_affiliation_string":"Plexus Team Qingdao Research and Development Center, Alcatel-Lucent Technologies, Inc., Qingdao, China","institution_ids":["https://openalex.org/I4210161003"]},{"raw_affiliation_string":"Alcatel-Lucent Technol., Murray Hill","institution_ids":["https://openalex.org/I1322087612"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100438045","display_name":"Zhiqiang Li","orcid":"https://orcid.org/0000-0001-8611-0742"},"institutions":[{"id":"https://openalex.org/I1322087612","display_name":"Alcatel Lucent (Germany)","ror":"https://ror.org/00c5mwp75","country_code":"DE","type":"company","lineage":["https://openalex.org/I1322087612"]},{"id":"https://openalex.org/I4210161003","display_name":"Qingdao Center of Resource Chemistry and New Materials","ror":"https://ror.org/05x85k702","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210089297","https://openalex.org/I4210161003"]}],"countries":["CN","DE"],"is_corresponding":false,"raw_author_name":"Zhiqiang Li","raw_affiliation_strings":["Plexus Team Qingdao Research and Development Center, Alcatel-Lucent Technologies, Inc., China","Plexus Team Qingdao R&D Center, Alcatel-Lucent Technologies"],"affiliations":[{"raw_affiliation_string":"Plexus Team Qingdao Research and Development Center, Alcatel-Lucent Technologies, Inc., China","institution_ids":["https://openalex.org/I4210161003"]},{"raw_affiliation_string":"Plexus Team Qingdao R&D Center, Alcatel-Lucent Technologies","institution_ids":["https://openalex.org/I1322087612"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084460856","display_name":"Shanqing Guo","orcid":"https://orcid.org/0000-0003-3367-0951"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shanqing Guo","raw_affiliation_strings":["Department of Computer Science, Shandong University, China","Department of Computer Science, Shandong University,"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Shandong University, China","institution_ids":["https://openalex.org/I154099455"]},{"raw_affiliation_string":"Department of Computer Science, Shandong University,","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049232116","display_name":"Taiming Feng","orcid":"https://orcid.org/0000-0002-5087-9565"},"institutions":[{"id":"https://openalex.org/I173911158","display_name":"Iowa State University","ror":"https://ror.org/04rswrd78","country_code":"US","type":"education","lineage":["https://openalex.org/I173911158"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Taiming Feng","raw_affiliation_strings":["Department of Computer Science, Iowa State University, USA","Department of Computer Science , Iowa State University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Iowa State University, USA","institution_ids":["https://openalex.org/I173911158"]},{"raw_affiliation_string":"Department of Computer Science , Iowa State University","institution_ids":["https://openalex.org/I173911158"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5014505438"],"corresponding_institution_ids":["https://openalex.org/I1322087612","https://openalex.org/I4210161003"],"apc_list":null,"apc_paid":null,"fwci":0.572,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.77422563,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1589","last_page":"1592"},"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":0.9998000264167786,"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/T12326","display_name":"Network Packet Processing and Optimization","score":0.9961000084877014,"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/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.82536780834198},{"id":"https://openalex.org/keywords/discretization","display_name":"Discretization","score":0.7000872492790222},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6760063171386719},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6682342290878296},{"id":"https://openalex.org/keywords/bayes-error-rate","display_name":"Bayes error rate","score":0.5602975487709045},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.5480435490608215},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5203742384910583},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.49627071619033813},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43511539697647095},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39456886053085327},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3692227005958557},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.29043084383010864},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.22302186489105225},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22135448455810547},{"id":"https://openalex.org/keywords/bayes-classifier","display_name":"Bayes classifier","score":0.15875259041786194}],"concepts":[{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.82536780834198},{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.7000872492790222},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6760063171386719},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6682342290878296},{"id":"https://openalex.org/C143809311","wikidata":"https://www.wikidata.org/wiki/Q4874458","display_name":"Bayes error rate","level":5,"score":0.5602975487709045},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.5480435490608215},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5203742384910583},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.49627071619033813},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43511539697647095},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39456886053085327},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3692227005958557},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.29043084383010864},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.22302186489105225},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22135448455810547},{"id":"https://openalex.org/C185207860","wikidata":"https://www.wikidata.org/wiki/Q17004744","display_name":"Bayes classifier","level":4,"score":0.15875259041786194},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnsc.2008.4525474","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnsc.2008.4525474","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Conference on Networking, Sensing and Control","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W105178432","https://openalex.org/W1524496078","https://openalex.org/W1524704912","https://openalex.org/W1538041071","https://openalex.org/W1661871015","https://openalex.org/W2012095206","https://openalex.org/W2144098589","https://openalex.org/W2149274049","https://openalex.org/W2171634548","https://openalex.org/W6604371904","https://openalex.org/W6631241307","https://openalex.org/W6631506987","https://openalex.org/W6632281367","https://openalex.org/W6636914306"],"related_works":["https://openalex.org/W2374047926","https://openalex.org/W179179905","https://openalex.org/W2394466068","https://openalex.org/W2360982908","https://openalex.org/W2086147528","https://openalex.org/W2393473353","https://openalex.org/W1973600295","https://openalex.org/W145653800","https://openalex.org/W4312309445","https://openalex.org/W2537862391"],"abstract_inverted_index":{"Accurate":[0],"network":[1,8],"traffic":[2,36],"classification":[3],"is":[4,63,74,99,114],"fundamental":[5],"to":[6,14,76,85,101],"numerous":[7],"activities,":[9],"from":[10,103],"quality":[11],"of":[12,44],"service":[13],"providing":[15],"operators":[16],"with":[17,42],"useful":[18],"forecasts":[19],"for":[20,34,111],"long-term":[21],"provisioning.":[22],"In":[23],"this":[24],"paper,":[25],"we":[26],"apply":[27],"the":[28,40,80,96],"discretization":[29,62,109],"method":[30,110,119],"in":[31,51,87],"Naive":[32,47,112],"Bayes":[33,48,113],"Internet":[35],"identification":[37],"and":[38,54,66,78],"compare":[39],"result":[41],"that":[43,61,108],"previously":[45],"applied":[46],"kernel":[49,69,81,118],"estimation":[50,82],"AUCKLAND":[52,92],"VI":[53,93],"Entry":[55,88],"data":[56,89,94],"sets.":[57,90],"Our":[58],"results":[59],"show":[60],"more":[64,115],"robust":[65],"accurate":[67],"than":[68,117],"estimation.":[70],"The":[71],"average":[72,97],"accuracy":[73,98],"improved":[75,100],"97.93%":[77],"outperforms":[79],"by":[83],"up":[84],"4.2%":[86],"For":[91],"sets,":[95],"90.37%":[102],"34.17%.":[104],"We":[105],"also":[106],"find":[107],"efficient":[116],"during":[120],"classification.":[121]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
