{"id":"https://openalex.org/W2528169092","doi":"https://doi.org/10.1109/dasc-picom-datacom-cyberscitec.2016.114","title":"Backbone Traffic Pattern Analysing Based on Joint Entropy","display_name":"Backbone Traffic Pattern Analysing Based on Joint Entropy","publication_year":2016,"publication_date":"2016-08-01","ids":{"openalex":"https://openalex.org/W2528169092","doi":"https://doi.org/10.1109/dasc-picom-datacom-cyberscitec.2016.114","mag":"2528169092"},"language":"en","primary_location":{"id":"doi:10.1109/dasc-picom-datacom-cyberscitec.2016.114","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dasc-picom-datacom-cyberscitec.2016.114","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100639068","display_name":"Jie Xu","orcid":"https://orcid.org/0000-0001-6632-7629"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Xu","raw_affiliation_strings":["College of Computer Science and Technology of South East University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology of South East University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100701995","display_name":"Wei Ding","orcid":"https://orcid.org/0000-0003-4402-5616"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Ding","raw_affiliation_strings":["College of Computer Science and Technology of South East University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology of South East University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069379086","display_name":"Jian Gong","orcid":"https://orcid.org/0000-0001-5786-713X"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Gong","raw_affiliation_strings":["College of Computer Science and Technology of South East University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology of South East University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090713660","display_name":"Xiaodong Zang","orcid":"https://orcid.org/0000-0002-8377-5877"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"XiaoDong Zang","raw_affiliation_strings":["College of Computer Science and Technology of South East University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology of South East University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10699503,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"8","issue":null,"first_page":"626","last_page":"633"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9995999932289124,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10138","display_name":"Network Traffic and Congestion Control","score":0.9983999729156494,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7524397373199463},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5829334855079651},{"id":"https://openalex.org/keywords/network-packet","display_name":"Network packet","score":0.5824384093284607},{"id":"https://openalex.org/keywords/backbone-network","display_name":"Backbone network","score":0.575509250164032},{"id":"https://openalex.org/keywords/router","display_name":"Router","score":0.5116572976112366},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4972541630268097},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.45004212856292725},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4463644027709961},{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.445865273475647},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4354771375656128},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.428339421749115},{"id":"https://openalex.org/keywords/airfield-traffic-pattern","display_name":"Airfield traffic pattern","score":0.41561418771743774},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.3275071978569031},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27988100051879883}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7524397373199463},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5829334855079651},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.5824384093284607},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.575509250164032},{"id":"https://openalex.org/C2775896111","wikidata":"https://www.wikidata.org/wiki/Q642560","display_name":"Router","level":2,"score":0.5116572976112366},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4972541630268097},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.45004212856292725},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4463644027709961},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.445865273475647},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4354771375656128},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.428339421749115},{"id":"https://openalex.org/C204673680","wikidata":"https://www.wikidata.org/wiki/Q1628107","display_name":"Airfield traffic pattern","level":2,"score":0.41561418771743774},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3275071978569031},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27988100051879883},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dasc-picom-datacom-cyberscitec.2016.114","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dasc-picom-datacom-cyberscitec.2016.114","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W256930182","https://openalex.org/W1233254008","https://openalex.org/W1481573196","https://openalex.org/W1965857701","https://openalex.org/W1970605442","https://openalex.org/W1977482721","https://openalex.org/W1982728146","https://openalex.org/W1986668511","https://openalex.org/W1989714227","https://openalex.org/W2002782745","https://openalex.org/W2013000316","https://openalex.org/W2036695131","https://openalex.org/W2037420005","https://openalex.org/W2065571333","https://openalex.org/W2066070767","https://openalex.org/W2072610025","https://openalex.org/W2073459066","https://openalex.org/W2083682556","https://openalex.org/W2086623401","https://openalex.org/W2101871381","https://openalex.org/W2102978741","https://openalex.org/W2127417949","https://openalex.org/W2146932554","https://openalex.org/W2152590494","https://openalex.org/W2152748561","https://openalex.org/W2154137957","https://openalex.org/W6668990524","https://openalex.org/W6682792823"],"related_works":["https://openalex.org/W1632806213","https://openalex.org/W2149721642","https://openalex.org/W2587362999","https://openalex.org/W2036532316","https://openalex.org/W432084041","https://openalex.org/W2394010358","https://openalex.org/W2361078351","https://openalex.org/W2986732134","https://openalex.org/W4239349137","https://openalex.org/W1463884142"],"abstract_inverted_index":{"There":[0],"are":[1,43,73,132],"hundreds":[2],"of":[3,79],"billions":[4],"packets":[5],"passing":[6],"through":[7],"the":[8,34,47,56,76,125],"network,":[9],"how":[10],"to":[11,32,45,75,105],"analyse":[12],"these":[13],"big":[14],"data":[15],"is":[16,103],"an":[17],"arduous":[18],"task.":[19],"In":[20],"this":[21,69,107],"paper":[22,70],"we":[23],"proposed":[24,67],"a":[25,61,89,100],"new":[26,40,62,108],"scheme":[27],"based":[28],"on":[29,124],"cluster":[30,90],"algorithm":[31,91],"explore":[33],"backbone":[35],"network":[36],"traffic":[37,41,80,83,97,126,130],"pattern.":[38],"Two":[39],"features":[42],"introduced":[44],"assist":[46],"analysing":[48],"process.":[49],"These":[50],"two":[51],"novel":[52,115],"attributes":[53,117],"can":[54],"represent":[55],"flow":[57,63,116],"effectively":[58],"cooperated":[59],"with":[60],"similarity":[64,119],"measurement":[65,120],"method":[66,121],"in":[68],"and":[71,118,128],"they":[72],"insensitive":[74],"random":[77],"fluctuation":[78],"size.":[81],"The":[82,110],"pattern":[84],"will":[85],"be":[86],"uncovered":[87],"by":[88],"derived":[92],"from":[93,99],"k-means.":[94],"Real":[95],"world":[96],"captured":[98],"border":[101],"router":[102],"used":[104],"test":[106],"method.":[109],"result":[111],"shows":[112],"that":[113],"our":[114],"perform":[122],"well":[123],"analysis":[127],"four":[129],"patterns":[131],"uncovered.":[133]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
