{"id":"https://openalex.org/W3162562977","doi":"https://doi.org/10.1109/icassp39728.2021.9414766","title":"Hybrid Model for Network Anomaly Detection with Gradient Boosting Decision Trees and Tabtransformer","display_name":"Hybrid Model for Network Anomaly Detection with Gradient Boosting Decision Trees and Tabtransformer","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3162562977","doi":"https://doi.org/10.1109/icassp39728.2021.9414766","mag":"3162562977"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9414766","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414766","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5032013640","display_name":"Xinyue Xu","orcid":"https://orcid.org/0000-0002-2037-5462"},"institutions":[{"id":"https://openalex.org/I118347636","display_name":"Australian National University","ror":"https://ror.org/019wvm592","country_code":"AU","type":"education","lineage":["https://openalex.org/I118347636"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Xinyue Xu","raw_affiliation_strings":["Australian National University,Canberra,Australia","Australian National University, Canberra, Australia"],"affiliations":[{"raw_affiliation_string":"Australian National University,Canberra,Australia","institution_ids":["https://openalex.org/I118347636"]},{"raw_affiliation_string":"Australian National University, Canberra, Australia","institution_ids":["https://openalex.org/I118347636"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109979028","display_name":"Xiaolu Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolu Zheng","raw_affiliation_strings":["Beihang University,Beijing,China","Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University,Beijing,China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5032013640"],"corresponding_institution_ids":["https://openalex.org/I118347636"],"apc_list":null,"apc_paid":null,"fwci":1.3754,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.81656283,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"8538","last_page":"8542"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9997000098228455,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9984999895095825,"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/boosting","display_name":"Boosting (machine learning)","score":0.7093281149864197},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6956825852394104},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.6400749087333679},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.6389120817184448},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5753587484359741},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5715879201889038},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5402467250823975},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5381835699081421},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4813145399093628},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4490816295146942},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.42822733521461487},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4023425877094269},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.2090786099433899}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7093281149864197},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6956825852394104},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.6400749087333679},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.6389120817184448},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5753587484359741},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5715879201889038},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5402467250823975},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5381835699081421},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4813145399093628},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4490816295146942},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.42822733521461487},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4023425877094269},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2090786099433899},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp39728.2021.9414766","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414766","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6399999856948853,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W273955616","https://openalex.org/W1625605225","https://openalex.org/W1678356000","https://openalex.org/W1964940342","https://openalex.org/W1977177161","https://openalex.org/W2144463230","https://openalex.org/W2295598076","https://openalex.org/W2555676925","https://openalex.org/W2613205839","https://openalex.org/W2755464334","https://openalex.org/W2768348081","https://openalex.org/W2783741806","https://openalex.org/W2886020981","https://openalex.org/W2911964244","https://openalex.org/W2912934387","https://openalex.org/W2963403868","https://openalex.org/W2964022491","https://openalex.org/W3001683812","https://openalex.org/W3034655362","https://openalex.org/W3035022492","https://openalex.org/W3048377462","https://openalex.org/W3088265803","https://openalex.org/W3094771832","https://openalex.org/W3102476541","https://openalex.org/W3111356309","https://openalex.org/W3135612867","https://openalex.org/W3217005176","https://openalex.org/W4212883601","https://openalex.org/W4385245566","https://openalex.org/W6610017368","https://openalex.org/W6637404493","https://openalex.org/W6739901393","https://openalex.org/W6745609711","https://openalex.org/W6750729320","https://openalex.org/W6786948829","https://openalex.org/W6791811571"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3137904399","https://openalex.org/W4287726157","https://openalex.org/W3040783393","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W2766514146","https://openalex.org/W4289703016","https://openalex.org/W2885516856","https://openalex.org/W4310224730"],"abstract_inverted_index":{"Last":[0],"decades":[1],"have":[2,15,171],"witnessed":[3],"the":[4,11,40,55,60,70,93,97,108,125,138,155,159,164,175,181,191],"monumental":[5],"growth":[6],"of":[7,28,57,166,183],"Inter-net":[8],"applications,":[9],"and":[10,52,118,135,185],"network":[12,88],"security":[13],"fields":[14],"attracted":[16],"increasing":[17],"interests":[18],"as":[19,90,187],"it":[20],"has":[21],"become":[22],"a":[23],"necessity":[24],"due":[25],"to":[26,38,54,106,157],"proliferation":[27],"information":[29],"technologies":[30],"in":[31,44,59,86,190],"everyday":[32],"life.":[33],"Net-work":[34],"anomaly":[35,109],"detection":[36,110],"aims":[37],"detect":[39],"potential":[41],"anomalous":[42],"behavior":[43],"traffic":[45,98],"data,":[46],"which":[47],"is":[48,131],"critical":[49],"for":[50,69,124],"detecting":[51,87],"reacting":[53],"presence":[56],"attacks":[58],"network.":[61],"In":[62],"this":[63],"pa-per,":[64],"we":[65,80,153],"present":[66,81],"our":[67,82,167],"solution":[68],"ICASSP":[71],"2021":[72],"Network":[73],"Anomaly":[74],"Detection":[75],"Challenge":[76],"(NAD)":[77],"challenge.":[78],"Firstly,":[79],"feature":[83],"extraction":[84],"method":[85],"anomalies,":[89],"efficiently":[91],"extracting":[92],"discriminate":[94],"features":[95],"from":[96],"data":[99],"over":[100],"time":[101],"can":[102,136,147],"be":[103],"greatly":[104],"helpful":[105],"improve":[107,158],"performance.":[111],"Secondly,":[112],"both":[113],"gradient":[114],"boosting":[115],"decision":[116],"trees":[117],"TabTransformer-Based":[119],"classification":[120,127],"model":[121],"are":[122],"trained":[123],"multi-class":[126],"task.":[128],"The":[129],"TabTransformer":[130],"based":[132],"on":[133,174],"self-attention":[134],"transform":[137],"categorical":[139],"feature\u2019":[140],"embeddings":[141],"into":[142],"robust":[143],"contextual":[144],"embeddings,":[145],"thus,":[146],"achieve":[148],"higher":[149],"prediction":[150],"accuracy.":[151],"Finally,":[152],"ensemble":[154],"results":[156],"performance":[160],"further.":[161],"To":[162],"demonstrate":[163],"effectiveness":[165],"approach,":[168],"extensive":[169],"experiments":[170],"been":[172],"conducted":[173],"NAD":[176],"datasets.":[177],"Our":[178],"approach":[179],"achieves":[180],"score":[182],"0.625":[184],"ranked":[186],"2nd":[188],"place":[189],"final":[192],"leaderboard.":[193]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2025-10-10T00:00:00"}