{"id":"https://openalex.org/W4200436835","doi":"https://doi.org/10.1109/icecit54077.2021.9641471","title":"A Comparative study of machine learning models for Network Intrusion Detection System using UNSW-NB 15 dataset","display_name":"A Comparative study of machine learning models for Network Intrusion Detection System using UNSW-NB 15 dataset","publication_year":2021,"publication_date":"2021-09-14","ids":{"openalex":"https://openalex.org/W4200436835","doi":"https://doi.org/10.1109/icecit54077.2021.9641471"},"language":"en","primary_location":{"id":"doi:10.1109/icecit54077.2021.9641471","doi":"https://doi.org/10.1109/icecit54077.2021.9641471","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecit54077.2021.9641471","pdf_url":null,"source":{"id":"https://openalex.org/S4363608437","display_name":"2021 International Conference on Electronics, Communications and Information Technology (ICECIT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Electronics, Communications and Information Technology (ICECIT)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5035231820","display_name":"Raisa Abedin Disha","orcid":"https://orcid.org/0000-0003-2157-1024","observed_orcids":["https://orcid.org/0000-0003-2157-1024"]},"institutions":[{"id":"https://openalex.org/I96426537","display_name":"Bangladesh University of Professionals","ror":"https://ror.org/01zphyp78","country_code":"BD","type":"education","lineage":["https://openalex.org/I96426537"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Raisa Abedin Disha","raw_affiliation_strings":["Dept. of Information and Communication Technology, Bangladesh University of Professionals, Dhaka, Bangladesh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Information and Communication Technology, Bangladesh University of Professionals, Dhaka, Bangladesh","institution_ids":["https://openalex.org/I96426537"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079330063","display_name":"Sajjad Waheed","orcid":"https://orcid.org/0000-0002-1525-376X","observed_orcids":["https://orcid.org/0000-0002-1525-376X"]},"institutions":[{"id":"https://openalex.org/I41173762","display_name":"Mawlana Bhashani Science and Technology University","ror":"https://ror.org/00gvj4587","country_code":"BD","type":"education","lineage":["https://openalex.org/I41173762"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Sajjad Waheed","raw_affiliation_strings":["Dept. of Information and Communication Technology, Mawlana Bhasani Science and Technology University, Tangail, Bangladesh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Information and Communication Technology, Mawlana Bhasani Science and Technology University, Tangail, Bangladesh","institution_ids":["https://openalex.org/I41173762"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.011,"has_fulltext":false,"cited_by_count":38,"citation_normalized_percentile":{"value":0.97131942,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9994999766349792,"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":0.9994999766349792,"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":0.0003000000142492354,"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"}},{"id":"https://openalex.org/T12391","display_name":"Artificial Immune Systems Applications","score":9.999999747378752e-05,"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-intrusion-detection","display_name":"network intrusion detection","score":0.9890000224113464},{"id":"https://openalex.org/keywords/unsw-nb15","display_name":"UNSW-NB15","score":0.8830000162124634},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"anomaly detection","score":0.8159999847412109},{"id":"https://openalex.org/keywords/decision-tree","display_name":"decision tree","score":0.7360000014305115},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":1.0},{"id":"https://openalex.org/keywords/gradient-boosted-decision-trees","display_name":"gradient boosting decision tree","score":0.8610000014305115},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"multilayer perceptron","score":0.9390000104904175},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.9300000071525574},{"id":"https://openalex.org/keywords/chi-square-feature-selection","display_name":"chi-square feature selection","score":0.675000011920929},{"id":"https://openalex.org/keywords/false-positive-rate","display_name":"false positive rate","score":0.8930000066757202},{"id":"https://openalex.org/keywords/network-security","display_name":"network security","score":0.6050000190734863}],"concepts":[{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.8207719922065735},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7587614059448242},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.6687336564064026},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6584120988845825},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6497793793678284},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6402541995048523},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6315403580665588},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5245033502578735},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.5155458450317383},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4929964244365692},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47736430168151855},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47127410769462585},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.41578489542007446},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3013363480567932}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icecit54077.2021.9641471","doi":"https://doi.org/10.1109/icecit54077.2021.9641471","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecit54077.2021.9641471","pdf_url":null,"source":{"id":"https://openalex.org/S4363608437","display_name":"2021 International Conference on Electronics, Communications and Information Technology (ICECIT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Electronics, Communications and Information Technology (ICECIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W433644524","https://openalex.org/W2015452969","https://openalex.org/W2296509296","https://openalex.org/W2678934292","https://openalex.org/W2730943916","https://openalex.org/W2762776925","https://openalex.org/W2947526192","https://openalex.org/W2950204666","https://openalex.org/W2958285686","https://openalex.org/W2998884377","https://openalex.org/W3003695670","https://openalex.org/W3004752684","https://openalex.org/W3040622027","https://openalex.org/W3106741970","https://openalex.org/W3124078042","https://openalex.org/W3179338414","https://openalex.org/W3210846950","https://openalex.org/W7149556586"],"related_works":["https://openalex.org/W3016038383","https://openalex.org/W4411793320","https://openalex.org/W4390506086","https://openalex.org/W7130705772","https://openalex.org/W3184866730","https://openalex.org/W7218508097","https://openalex.org/W6901685142","https://openalex.org/W3168793901","https://openalex.org/W4391342026","https://openalex.org/W7117575127"],"abstract_inverted_index":{"In":[0,38],"recent":[1],"days,":[2],"the":[3,30,53,77,90,106,111,124],"Intrusion":[4],"Detection":[5],"System":[6],"(IDS)":[7],"has":[8],"become":[9],"a":[10],"fundamental":[11],"component":[12],"of":[13,52,55,116,165],"network":[14,32],"security":[15],"for":[16,26,137,141],"an":[17,72],"organization.":[18],"Several":[19],"approaches":[20],"have":[21],"been":[22],"proposed":[23,154],"and":[24,33,88,101,130],"developed":[25],"IDS":[27],"to":[28,48,58,75,158],"protect":[29],"perimeter":[31],"resources":[34],"from":[35],"different":[36],"cyber-attacks.":[37],"this":[39],"work,":[40],"we":[41,66,109],"concentrated":[42],"on":[43],"machine":[44],"learning":[45],"(ML)":[46],"techniques":[47],"build":[49],"IDS,":[50],"because":[51],"effectiveness":[54],"ML":[56,80,161],"models":[57,143],"provide":[59],"better":[60],"accuracy":[61,129],"in":[62,163],"anomaly":[63],"detection.":[64],"However,":[65,118],"used":[67],"UNSW-NB":[68],"15":[69],"dataset":[70,74],"as":[71,123],"offline":[73],"evaluate":[76],"binary":[78],"classification-based":[79],"models.":[81],"Performance":[82],"analysis":[83,150],"was":[84,120,144],"conducted":[85],"by":[86],"training":[87],"testing":[89],"Decision":[91],"Tree":[92,99],"(DT),":[93],"Random":[94],"Forest":[95],"(RF),":[96],"Gradient":[97],"Boosting":[98],"(GBT),":[100],"Multi-Layer":[102],"Perceptron":[103],"(MLP).":[104],"Using":[105],"Chi-Square":[107],"test,":[108],"removed":[110],"features":[112],"which":[113],"were":[114],"independent":[115],"response.":[117],"DT":[119],"found":[121],"out":[122],"best":[125],"classifier":[126],"with":[127,146],"maximum":[128],"lowest":[131],"False":[132],"Positive":[133],"Rate":[134],"(FPR).":[135],"Except":[136],"RF,":[138],"overall":[139],"performance":[140],"all":[142],"improved":[145],"feature":[147],"elimination.":[148],"Experimental":[149],"revealed":[151],"that":[152],"our":[153],"approach":[155],"is":[156],"superior":[157],"other":[159],"existing":[160],"methods":[162],"terms":[164],"accuracy.":[166]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2021-12-31T00:00:00"}
