{"id":"https://openalex.org/W4417360471","doi":"https://doi.org/10.3390/jcp5040112","title":"Statistical and Multivariate Analysis of the IoT-23 Dataset: A Comprehensive Approach to Network Traffic Pattern Discovery","display_name":"Statistical and Multivariate Analysis of the IoT-23 Dataset: A Comprehensive Approach to Network Traffic Pattern Discovery","publication_year":2025,"publication_date":"2025-12-16","ids":{"openalex":"https://openalex.org/W4417360471","doi":"https://doi.org/10.3390/jcp5040112"},"language":"en","primary_location":{"id":"doi:10.3390/jcp5040112","is_oa":true,"landing_page_url":"https://doi.org/10.3390/jcp5040112","pdf_url":"https://www.mdpi.com/2624-800X/5/4/112/pdf?version=1765881113","source":{"id":"https://openalex.org/S4210232532","display_name":"Journal of Cybersecurity and Privacy","issn_l":"2624-800X","issn":["2624-800X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Cybersecurity and Privacy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2624-800X/5/4/112/pdf?version=1765881113","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111019612","display_name":"Humera Ghani","orcid":null},"institutions":[{"id":"https://openalex.org/I126193024","display_name":"London Metropolitan University","ror":"https://ror.org/00ae33288","country_code":"GB","type":"education","lineage":["https://openalex.org/I126193024"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Humera Ghani","raw_affiliation_strings":["School of Computing and Digital Media, London Metropolitan University, London N7 8DB, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Digital Media, London Metropolitan University, London N7 8DB, UK","institution_ids":["https://openalex.org/I126193024"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000146099","display_name":"Shahram Salekzamankhani","orcid":null},"institutions":[{"id":"https://openalex.org/I126193024","display_name":"London Metropolitan University","ror":"https://ror.org/00ae33288","country_code":"GB","type":"education","lineage":["https://openalex.org/I126193024"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Shahram Salekzamankhani","raw_affiliation_strings":["School of Computing and Digital Media, London Metropolitan University, London N7 8DB, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Digital Media, London Metropolitan University, London N7 8DB, UK","institution_ids":["https://openalex.org/I126193024"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079514173","display_name":"Bal S. Virdee","orcid":"https://orcid.org/0000-0001-7203-0039"},"institutions":[{"id":"https://openalex.org/I126193024","display_name":"London Metropolitan University","ror":"https://ror.org/00ae33288","country_code":"GB","type":"education","lineage":["https://openalex.org/I126193024"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Bal Virdee","raw_affiliation_strings":["School of Computing and Digital Media, London Metropolitan University, London N7 8DB, UK"],"raw_orcid":"https://orcid.org/0000-0001-7203-0039","affiliations":[{"raw_affiliation_string":"School of Computing and Digital Media, London Metropolitan University, London N7 8DB, UK","institution_ids":["https://openalex.org/I126193024"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5111019612"],"corresponding_institution_ids":["https://openalex.org/I126193024"],"apc_list":{"value":1000,"currency":"CHF","value_usd":1207},"apc_paid":{"value":1000,"currency":"CHF","value_usd":1207},"fwci":0.5872,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.75624974,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"5","issue":"4","first_page":"112","last_page":"112"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.3521000146865845,"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":0.3521000146865845,"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":0.3052999973297119,"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/T10714","display_name":"Software-Defined Networks and 5G","score":0.022299999371170998,"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/principal-component-analysis","display_name":"Principal component analysis","score":0.5863999724388123},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5324000120162964},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4702000021934509},{"id":"https://openalex.org/keywords/dbscan","display_name":"DBSCAN","score":0.459199994802475},{"id":"https://openalex.org/keywords/descriptive-statistics","display_name":"Descriptive statistics","score":0.3968000113964081},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.39239999651908875},{"id":"https://openalex.org/keywords/skewness","display_name":"Skewness","score":0.3781999945640564},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.35679998993873596},{"id":"https://openalex.org/keywords/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.3515999913215637}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6912999749183655},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6420999765396118},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5863999724388123},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5324000120162964},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4702000021934509},{"id":"https://openalex.org/C46576248","wikidata":"https://www.wikidata.org/wiki/Q1114630","display_name":"DBSCAN","level":5,"score":0.459199994802475},{"id":"https://openalex.org/C39896193","wikidata":"https://www.wikidata.org/wiki/Q380344","display_name":"Descriptive statistics","level":2,"score":0.3968000113964081},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.39239999651908875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3806000053882599},{"id":"https://openalex.org/C122342681","wikidata":"https://www.wikidata.org/wiki/Q330828","display_name":"Skewness","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.35679998993873596},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.3515999913215637},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35030001401901245},{"id":"https://openalex.org/C2781317605","wikidata":"https://www.wikidata.org/wiki/Q7832483","display_name":"Traffic analysis","level":2,"score":0.34700000286102295},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.3321000039577484},{"id":"https://openalex.org/C32946077","wikidata":"https://www.wikidata.org/wiki/Q618079","display_name":"Network analysis","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.3158000111579895},{"id":"https://openalex.org/C38180746","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate analysis","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C2986587452","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical analysis","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C182590292","wikidata":"https://www.wikidata.org/wiki/Q989632","display_name":"Network security","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.2587999999523163},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C71176878","wikidata":"https://www.wikidata.org/wiki/Q17014987","display_name":"Functional principal component analysis","level":3,"score":0.2515999972820282}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/jcp5040112","is_oa":true,"landing_page_url":"https://doi.org/10.3390/jcp5040112","pdf_url":"https://www.mdpi.com/2624-800X/5/4/112/pdf?version=1765881113","source":{"id":"https://openalex.org/S4210232532","display_name":"Journal of Cybersecurity and Privacy","issn_l":"2624-800X","issn":["2624-800X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Cybersecurity and Privacy","raw_type":"journal-article"},{"id":"pmh:oai:repository.londonmet.ac.uk:11136","is_oa":true,"landing_page_url":"https://repository.londonmet.ac.uk/11136/1/jcp-05-00112.pdf","pdf_url":"https://repository.londonmet.ac.uk/11136/1/jcp-05-00112.pdf","source":{"id":"https://openalex.org/S4306400140","display_name":"London Met Repository (London Metropolitan University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I126193024","host_organization_name":"London Metropolitan University","host_organization_lineage":["https://openalex.org/I126193024"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"pmh:oai:doaj.org/article:9bef0e63c9a04368b161af4fa2c57a32","is_oa":true,"landing_page_url":"https://doaj.org/article/9bef0e63c9a04368b161af4fa2c57a32","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Cybersecurity and Privacy, Vol 5, Iss 4, p 112 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/jcp5040112","is_oa":true,"landing_page_url":"https://doi.org/10.3390/jcp5040112","pdf_url":"https://www.mdpi.com/2624-800X/5/4/112/pdf?version=1765881113","source":{"id":"https://openalex.org/S4210232532","display_name":"Journal of Cybersecurity and Privacy","issn_l":"2624-800X","issn":["2624-800X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Cybersecurity and Privacy","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4417360471.pdf","grobid_xml":"https://content.openalex.org/works/W4417360471.grobid-xml"},"referenced_works_count":9,"referenced_works":["https://openalex.org/W2334853001","https://openalex.org/W3024012711","https://openalex.org/W3164964481","https://openalex.org/W4292069033","https://openalex.org/W4390467010","https://openalex.org/W4400468565","https://openalex.org/W4404239341","https://openalex.org/W4409360552","https://openalex.org/W4410991897"],"related_works":[],"abstract_inverted_index":{"The":[0,62,241],"rapid":[1],"expansion":[2],"of":[3,5,19,40,54,94,108,116,164,177,206,230,261],"Internet":[4],"Things":[6],"(IoT)":[7],"technologies":[8],"has":[9],"introduced":[10],"significant":[11,183],"challenges":[12],"in":[13,248],"understanding":[14],"the":[15,41,52,84,99,178,207,227,259],"complexity":[16],"and":[17,37,50,69,79,103,111,113,119,142,144,152,181,225,232],"structure":[18],"network":[20,47,194,238],"traffic":[21,48,195,239],"data,":[22],"which":[23],"is":[24,172],"essential":[25],"for":[26,58,97,212,220,234,246,253],"developing":[27],"effective":[28],"cybersecurity":[29,223,264],"solutions.":[30,265],"This":[31],"research":[32,224],"presents":[33],"a":[34,135,145],"comprehensive":[35],"statistical":[36,251],"multivariate":[38,70],"analysis":[39,81,90,133],"IoT-23":[42,87],"dataset":[43,255],"to":[44,83],"identify":[45],"meaningful":[46],"patterns":[49],"assess":[51],"effectiveness":[53,229],"various":[55],"analytical":[56],"methods":[57,252],"IoT":[59,198,222,237,254],"security":[60],"research.":[61],"study":[63],"applies":[64],"descriptive":[65],"statistics,":[66],"inferential":[67,155],"analysis,":[68,256],"techniques,":[71],"including":[72],"Principal":[73],"Component":[74],"Analysis":[75],"(PCA),":[76],"DBSCAN":[77,186,233],"clustering,":[78],"factor":[80],"(FA),":[82],"publicly":[85],"available":[86],"dataset.":[88,214],"Descriptive":[89],"reveals":[91],"clear":[92],"evidence":[93],"non-normal":[95],"distributions:":[96],"example,":[98],"features":[100],"src_bytes,":[101],"dst_bytes,":[102],"src_pkts":[104],"have":[105],"skewness":[106],"values":[107,115,123],"\u22124.21,":[109],"\u22123.87,":[110],"\u22122.98,":[112],"kurtosis":[114],"38.45,":[117],"29.67,":[118],"18.23,":[120],"respectively.":[121],"These":[122,215],"indicate":[124,157],"highly":[125,173],"skewed,":[126],"heavy-tailed":[127],"distributions":[128],"with":[129],"frequent":[130],"outliers.":[131],"Correlation":[132],"revealed":[134],"strong":[136,146],"positive":[137],"correlation":[138,148],"(0.97)":[139],"between":[140,150],"orig_bytes":[141],"resp_bytes,":[143,153],"negative":[147],"(\u22120.76)":[149],"duration":[151],"while":[154],"statistics":[156],"that":[158,170],"linear":[159],"regression":[160],"provides":[161],"optimal":[162],"modeling":[163],"data":[165],"relationships.":[166],"Key":[167],"findings":[168,242],"show":[169],"PCA":[171,231],"effective,":[174],"capturing":[175],"99%":[176],"dataset\u2019s":[179],"variance":[180],"enabling":[182],"dimensionality":[184],"reduction.":[185],"clustering":[187],"identifies":[188],"six":[189],"distinct":[190],"clusters,":[191],"highlighting":[192],"diverse":[193],"behaviors":[196],"within":[197],"environments.":[199],"In":[200],"contrast,":[201],"FA":[202],"explains":[203],"only":[204],"11.63%":[205],"variance,":[208],"indicating":[209],"limited":[210],"suitability":[211],"this":[213],"results":[216],"establish":[217],"important":[218],"benchmarks":[219],"future":[221],"demonstrate":[226],"superior":[228],"analyzing":[235],"complex":[236],"data.":[240],"offer":[243],"practical":[244],"guidance":[245],"researchers":[247],"selecting":[249],"appropriate":[250],"ultimately":[257],"supporting":[258],"development":[260],"more":[262],"robust":[263]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-12-16T00:00:00"}
