{"id":"https://openalex.org/W3045576254","doi":null,"title":"Zero-Day Traffic Identification Using One-Dimension Convolutional Neural Networks And Auto Encoder Machine","display_name":"Zero-Day Traffic Identification Using One-Dimension Convolutional Neural Networks And Auto Encoder Machine","publication_year":2020,"publication_date":"2020-06-22","ids":{"openalex":"https://openalex.org/W3045576254","mag":"3045576254"},"language":"en","primary_location":{"id":"mag:3045576254","doi":null,"is_oa":false,"landing_page_url":"https://dblp.uni-trier.de/db/conf/networking/networking2020.html#JinXCYLW20","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"2020 IFIP Networking Conference (Networking)","raw_type":null},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Dong Jin","orcid":"https://orcid.org/0000-0001-9955-1432","observed_orcids":["https://orcid.org/0000-0001-9955-1432"]},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Jin","raw_affiliation_strings":["University of, Science and Technology of China, Hefei, China#TAB#"],"raw_orcid":"https://orcid.org/0000-0001-9955-1432","affiliations":[{"raw_affiliation_string":"University of, Science and Technology of China, Hefei, China#TAB#","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100681686","display_name":"Jinsen Xie","orcid":"https://orcid.org/0000-0003-3540-4825","observed_orcids":["https://orcid.org/0000-0003-3540-4825"]},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinsen Xie","raw_affiliation_strings":["University of, Science and Technology of China, Hefei, China#TAB#"],"raw_orcid":"https://orcid.org/0000-0003-3540-4825","affiliations":[{"raw_affiliation_string":"University of, Science and Technology of China, Hefei, China#TAB#","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045720734","display_name":"Shuangwu Chen","orcid":"https://orcid.org/0000-0003-2817-9738","observed_orcids":["https://orcid.org/0000-0003-2817-9738"]},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuangwu Chen","raw_affiliation_strings":["University of, Science and Technology of China, Hefei, China#TAB#"],"raw_orcid":"https://orcid.org/0000-0003-2817-9738","affiliations":[{"raw_affiliation_string":"University of, Science and Technology of China, Hefei, China#TAB#","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083577000","display_name":"Jian Yang","orcid":"https://orcid.org/0000-0002-7329-4738","observed_orcids":["https://orcid.org/0000-0002-7329-4738"]},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Yang","raw_affiliation_strings":["University of, Science and Technology of China, Hefei, China#TAB#"],"raw_orcid":"https://orcid.org/0000-0002-7329-4738","affiliations":[{"raw_affiliation_string":"University of, Science and Technology of China, Hefei, China#TAB#","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025531922","display_name":"Xinmin Liu","orcid":"https://orcid.org/0000-0002-5410-2796","observed_orcids":["https://orcid.org/0000-0002-5410-2796"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinmin Liu","raw_affiliation_strings":["New H3C Technologies Co,Beijing,China"],"raw_orcid":"https://orcid.org/0000-0002-5410-2796","affiliations":[{"raw_affiliation_string":"New H3C Technologies Co,Beijing,China","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-8180-2886","observed_orcids":["https://orcid.org/0000-0002-8180-2886"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["New H3C Technologies Co,Beijing,China"],"raw_orcid":"https://orcid.org/0000-0002-8180-2886","affiliations":[{"raw_affiliation_string":"New H3C Technologies Co,Beijing,China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2388,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.4630955,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"559","last_page":"563"},"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":0.0,"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/network-traffic-classification","display_name":"network traffic classification","score":0.9649999737739563},{"id":"https://openalex.org/keywords/1d-convolutional-neural-network","display_name":"1D convolutional neural network","score":0.8899999856948853},{"id":"https://openalex.org/keywords/autoencoder-reconstruction-error","display_name":"autoencoder reconstruction error","score":0.8690000176429749},{"id":"https://openalex.org/keywords/extreme-value-theory","display_name":"extreme value theory","score":0.8759999871253967},{"id":"https://openalex.org/keywords/confidence-estimation","display_name":"confidence estimation","score":0.875},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"anomaly detection","score":0.6499999761581421}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7047683000564575},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6868869066238403},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6266997456550598},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.538165807723999},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5193101763725281},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5118629336357117},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48490458726882935},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.473576158285141},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.450260728597641},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4302125573158264},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4105028808116913},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32124078273773193},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3045576254","doi":null,"is_oa":false,"landing_page_url":"https://dblp.uni-trier.de/db/conf/networking/networking2020.html#JinXCYLW20","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"2020 IFIP Networking Conference (Networking)","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.5378999710083008,"id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1973668864","https://openalex.org/W2018459374","https://openalex.org/W2161251616","https://openalex.org/W2343828539","https://openalex.org/W2606697812","https://openalex.org/W2743678626","https://openalex.org/W2753434589","https://openalex.org/W2771013472","https://openalex.org/W2897202622","https://openalex.org/W2904566708","https://openalex.org/W2910571091","https://openalex.org/W2921187422","https://openalex.org/W2979687053"],"related_works":["https://openalex.org/W2431812255","https://openalex.org/W119611611","https://openalex.org/W4385955433","https://openalex.org/W2953874026","https://openalex.org/W3208904656","https://openalex.org/W4313310398","https://openalex.org/W3149988673","https://openalex.org/W7213988104","https://openalex.org/W3037188667","https://openalex.org/W2967001080"],"abstract_inverted_index":{"Network":[0],"traffic":[1,8,13,48,57,131,175],"identification":[2,64,132,149,162,186],"plays":[3],"an":[4,106,153],"important":[5],"role":[6],"in":[7,29,51,185],"engineering,":[9],"anomaly":[10],"detection":[11],"and":[12,19,38,87,140],"billing.":[14],"Recently,":[15],"the":[16,31,35,39,46,55,68,82,85,102,111,148,157,161,166,189],"machine":[17],"learning":[18,21],"deep":[20],"based":[22,100,164],"algorithms":[23],"have":[24],"made":[25],"a":[26,52,62,115,124,129,182],"great":[27,183],"success":[28],"identifying":[30],"known":[32,86,125],"applications,":[33],"where":[34],"training":[36],"set":[37,41],"test":[40],"are":[42],"supposed":[43],"to":[44,67,75,80,145,155],"contain":[45],"same":[47],"classes.":[49],"However,":[50],"realistic":[53,174],"scenario,":[54],"network":[56],"classifier":[58],"may":[59],"suffer":[60],"from":[61],"low":[63],"accuracy":[65,187],"due":[66],"substantial":[69],"zero-day":[70,88,116,130],"(unknown)":[71],"traffic.":[72],"The":[73,170],"essential":[74],"solve":[76],"this":[77,98],"problem":[78],"is":[79,118],"find":[81],"boundary":[83],"between":[84],"traffic,":[89],"which":[90],"has":[91,181],"not":[92],"been":[93],"well":[94],"studied":[95],"before.":[96],"In":[97,143],"paper,":[99],"on":[101,165,173],"fact":[103],"that":[104,122,178],"for":[105],"Auto":[107],"Encoder":[108],"(AE)":[109],"machine,":[110],"reconstruction":[112],"error":[113],"of":[114,123,160],"class":[117],"generally":[119],"larger":[120],"than":[121,188],"class,":[126],"we":[127,151],"propose":[128,152],"method":[133,180],"using":[134],"one-Dimension":[135],"Convolutional":[136],"Neural":[137],"Networks":[138],"(1D-CNN)":[139],"AE":[141],"machine.":[142],"order":[144],"further":[146],"improve":[147],"accuracy,":[150],"algorithm":[154],"estimate":[156],"confidence":[158],"possibility":[159],"results":[163],"Extreme":[167],"Value":[168],"Theory.":[169],"experiments":[171],"conducted":[172],"datasets":[176],"demonstrate":[177],"our":[179],"improvement":[184],"benchmarks.":[190]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2022,"cited_by_count":3}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2020-08-03T00:00:00"}