{"id":"https://openalex.org/W2962608904","doi":"https://doi.org/10.1109/iccw.2019.8756996","title":"Abnormal Network Traffic Detection Based on Transfer Component Analysis","display_name":"Abnormal Network Traffic Detection Based on Transfer Component Analysis","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W2962608904","doi":"https://doi.org/10.1109/iccw.2019.8756996","mag":"2962608904"},"language":"en","primary_location":{"id":"doi:10.1109/iccw.2019.8756996","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccw.2019.8756996","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Communications Workshops (ICC Workshops)","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/A5101878196","display_name":"Jie Niu","orcid":"https://orcid.org/0000-0001-8544-7474"},"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":true,"raw_author_name":"Jie Niu","raw_affiliation_strings":["School of Electronic Engineering, Beijing University of Posts and Telecommunication"],"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Beijing University of Posts and Telecommunication","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100642156","display_name":"Yong Zhang","orcid":"https://orcid.org/0000-0003-4997-698X"},"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":"Yong Zhang","raw_affiliation_strings":["School of Electronic Engineering, Beijing University of Posts and Telecommunication"],"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Beijing University of Posts and Telecommunication","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060179254","display_name":"Dan Liu","orcid":"https://orcid.org/0000-0001-6479-9446"},"institutions":[{"id":"https://openalex.org/I4210142656","display_name":"Instrumentation Technology and Economy Institute","ror":"https://ror.org/04983b693","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210142656"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dan Liu","raw_affiliation_strings":["Instrumentation Technology and economy Institute, Beijing, P.R.C"],"affiliations":[{"raw_affiliation_string":"Instrumentation Technology and economy Institute, Beijing, P.R.C","institution_ids":["https://openalex.org/I4210142656"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028821253","display_name":"Da Guo","orcid":"https://orcid.org/0000-0001-9564-2994"},"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":"Da Guo","raw_affiliation_strings":["School of Electronic Engineering, Beijing University of Posts and Telecommunication"],"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Beijing University of Posts and Telecommunication","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081117495","display_name":"Yinglei Teng","orcid":"https://orcid.org/0000-0002-7170-4764"},"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":"Yinglei Teng","raw_affiliation_strings":["School of Electronic Engineering, Beijing University of Posts and Telecommunication"],"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Beijing University of Posts and Telecommunication","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101878196"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":1.5917,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.84917722,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9998999834060669,"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":0.9998999834060669,"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/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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9947999715805054,"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":0.8056144714355469},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.7290552854537964},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6538512110710144},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6275190711021423},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5230228304862976},{"id":"https://openalex.org/keywords/traffic-classification","display_name":"Traffic classification","score":0.5147035717964172},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5139877200126648},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4926488995552063},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46563851833343506},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.4631838798522949},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4251518249511719},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4221593737602234},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.41187116503715515}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8056144714355469},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.7290552854537964},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6538512110710144},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6275190711021423},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5230228304862976},{"id":"https://openalex.org/C169988225","wikidata":"https://www.wikidata.org/wiki/Q7832484","display_name":"Traffic classification","level":3,"score":0.5147035717964172},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5139877200126648},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4926488995552063},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46563851833343506},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.4631838798522949},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4251518249511719},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4221593737602234},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.41187116503715515},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.0},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccw.2019.8756996","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccw.2019.8756996","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Communications Workshops (ICC Workshops)","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":18,"referenced_works":["https://openalex.org/W1901832985","https://openalex.org/W2031047963","https://openalex.org/W2100664256","https://openalex.org/W2115403315","https://openalex.org/W2164943005","https://openalex.org/W2342408547","https://openalex.org/W2537766808","https://openalex.org/W2592680600","https://openalex.org/W2608546004","https://openalex.org/W2613548641","https://openalex.org/W2752291283","https://openalex.org/W2759910885","https://openalex.org/W2785474195","https://openalex.org/W2789828921","https://openalex.org/W2792186710","https://openalex.org/W2897679951","https://openalex.org/W2901543039","https://openalex.org/W6704694796"],"related_works":["https://openalex.org/W1980381208","https://openalex.org/W2364594919","https://openalex.org/W2167092671","https://openalex.org/W1861706286","https://openalex.org/W2219338811","https://openalex.org/W2149583853","https://openalex.org/W3162204513","https://openalex.org/W2143002539","https://openalex.org/W4293472652","https://openalex.org/W2130386332"],"abstract_inverted_index":{"Machine":[0],"learning":[1,74,143],"based":[2,91,171],"abnormal":[3,79,166],"traffic":[4,40,81,125,134,168],"detection":[5,59,82,89,169],"schemes":[6,170],"require":[7],"related":[8],"training":[9],"and":[10,26,61,84,121],"test":[11],"datasets":[12,97,135],"to":[13,20,36,57,78,102,155],"have":[14],"the":[15,23,30,37,42,50,53,65,72,103,109,114,118,123,140,145,161,165],"same":[16,104],"feature":[17,27],"distribution.":[18],"Due":[19],"differences":[21],"of":[22,52,64,98,147,164],"dataset":[24],"types":[25],"distributions,":[28],"when":[29],"trained":[31,112],"classification":[32,66],"model":[33,110],"is":[34,76,111],"applied":[35,77],"new":[38,124],"network":[39,80,87,167],"datasets,":[41],"valid":[43],"identification":[44],"cannot":[45],"be":[46,151],"achieved,":[47],"resulting":[48],"in":[49,117],"failure":[51],"model.":[54],"In":[55],"order":[56],"enhance":[58],"accuracy":[60,146],"generalization":[62],"performance":[63],"model,":[67],"this":[68],"paper":[69],"investigates":[70],"how":[71],"transfer":[73,93],"theory":[75],"system":[83],"proposes":[85],"a":[86],"intrusion":[88],"method":[90,149],"on":[92,172],"component":[94],"analysis.":[95],"With":[96],"different":[99,129,133],"distributions":[100],"mapped":[101],"subspace":[105,120],"by":[106,153],"domain":[107],"adaptation,":[108],"with":[113,139],"base":[115],"classifiers":[116],"shared":[119],"detects":[122],"data":[126],"generated":[127],"from":[128],"domains.":[130],"Experiments":[131],"involving":[132],"show":[136],"that,":[137],"compared":[138],"traditional":[141],"machine":[142,173],"method,":[144],"our":[148],"can":[150,158],"increased":[152],"up":[154],"75%.":[156],"It":[157],"also":[159],"extend":[160],"application":[162],"range":[163],"learning.":[174]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}