{"id":"https://openalex.org/W7134168342","doi":"https://doi.org/10.1016/j.csi.2026.104156","title":"CLU-ID: Contrastive learning based unsupervised intrusion detection for low-power IoT devices","display_name":"CLU-ID: Contrastive learning based unsupervised intrusion detection for low-power IoT devices","publication_year":2026,"publication_date":"2026-03-07","ids":{"openalex":"https://openalex.org/W7134168342","doi":"https://doi.org/10.1016/j.csi.2026.104156"},"language":"en","primary_location":{"id":"doi:10.1016/j.csi.2026.104156","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.csi.2026.104156","pdf_url":null,"source":{"id":"https://openalex.org/S119630662","display_name":"Computer Standards & Interfaces","issn_l":"0920-5489","issn":["0920-5489","1872-7018"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Standards &amp; Interfaces","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.csi.2026.104156","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101982764","display_name":"Yi Chen","orcid":"https://orcid.org/0009-0002-9002-0779"},"institutions":[{"id":"https://openalex.org/I4210157581","display_name":"Jiangsu Police Officer College","ror":"https://ror.org/04k1m2t10","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210157581"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Chen","raw_affiliation_strings":["Department of Computer and Information Security Management and the Collaborative Innovation Research Center of Intelligent Policing, Fujian Police College, Fuzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Security Management and the Collaborative Innovation Research Center of Intelligent Policing, Fujian Police College, Fuzhou, China","institution_ids":["https://openalex.org/I4210157581"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128365504","display_name":"Wenxi Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxi Liu","raw_affiliation_strings":["College of Computer and Data Science, Fuzhou University, Fuzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128340074","display_name":"Xu Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I354108","display_name":"Minjiang University","ror":"https://ror.org/00s7tkw17","country_code":"CN","type":"education","lineage":["https://openalex.org/I354108"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xu Yang","raw_affiliation_strings":["College of Computer and Data Science, Minjiang University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-2735-2359","affiliations":[{"raw_affiliation_string":"College of Computer and Data Science, Minjiang University, Fuzhou, China","institution_ids":["https://openalex.org/I354108"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5128353678","display_name":"Wencheng Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I185523456","display_name":"University of Southern Queensland","ror":"https://ror.org/04sjbnx57","country_code":"AU","type":"education","lineage":["https://openalex.org/I185523456"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Wencheng Yang","raw_affiliation_strings":["School of Mathematics, Physics and Computing, University of Southern Queensland, Queensland, Australia"],"raw_orcid":"https://orcid.org/0000-0001-7800-2215","affiliations":[{"raw_affiliation_string":"School of Mathematics, Physics and Computing, University of Southern Queensland, Queensland, Australia","institution_ids":["https://openalex.org/I185523456"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5128340074"],"corresponding_institution_ids":["https://openalex.org/I354108"],"apc_list":{"value":3000,"currency":"USD","value_usd":3000},"apc_paid":{"value":3000,"currency":"USD","value_usd":3000},"fwci":25.2212,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.99243066,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"98","issue":null,"first_page":"104156","last_page":"104156"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.8442000150680542,"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":0.8442000150680542,"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":0.04100000113248825,"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":0.029600000008940697,"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/autoencoder","display_name":"Autoencoder","score":0.7838000059127808},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7063000202178955},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.633400022983551},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.45590001344680786},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4458000063896179},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4269999861717224},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3837999999523163},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.36980000138282776}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.859000027179718},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7838000059127808},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7063000202178955},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6427000164985657},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.633400022983551},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.45590001344680786},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4458000063896179},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4269999861717224},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4237000048160553},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4214000105857849},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3837999999523163},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.36980000138282776},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3490999937057495},{"id":"https://openalex.org/C149810388","wikidata":"https://www.wikidata.org/wiki/Q5374873","display_name":"Emulation","level":2,"score":0.34119999408721924},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C139502532","wikidata":"https://www.wikidata.org/wiki/Q1122090","display_name":"Computational intelligence","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C2777629044","wikidata":"https://www.wikidata.org/wiki/Q614959","display_name":"Contrastive analysis","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.28349998593330383}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.csi.2026.104156","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.csi.2026.104156","pdf_url":null,"source":{"id":"https://openalex.org/S119630662","display_name":"Computer Standards & Interfaces","issn_l":"0920-5489","issn":["0920-5489","1872-7018"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Standards &amp; Interfaces","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.csi.2026.104156","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.csi.2026.104156","pdf_url":null,"source":{"id":"https://openalex.org/S119630662","display_name":"Computer Standards & Interfaces","issn_l":"0920-5489","issn":["0920-5489","1872-7018"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Standards &amp; Interfaces","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1242748811","https://openalex.org/W2014268383","https://openalex.org/W2129281431","https://openalex.org/W2144182447","https://openalex.org/W2296719434","https://openalex.org/W2799758613","https://openalex.org/W2978153077","https://openalex.org/W2982577861","https://openalex.org/W3151784567","https://openalex.org/W4206490792","https://openalex.org/W4212986808","https://openalex.org/W4220959892","https://openalex.org/W4283210230","https://openalex.org/W4293093536","https://openalex.org/W4312823356","https://openalex.org/W4323275401","https://openalex.org/W4367188982","https://openalex.org/W4382281941","https://openalex.org/W4387872885","https://openalex.org/W4388505134","https://openalex.org/W4396769190","https://openalex.org/W4399729483","https://openalex.org/W4400524938","https://openalex.org/W4401508167","https://openalex.org/W4401508391","https://openalex.org/W4401508524","https://openalex.org/W4409474994"],"related_works":[],"abstract_inverted_index":{"The":[0,89,123],"proliferation":[1],"of":[2,4,22,29,59,112,131],"Internet":[3],"Things":[5],"(IoT)":[6],"devices":[7,24,53],"raises":[8],"significant":[9],"security":[10],"concerns,":[11],"yet":[12],"existing":[13],"anomaly":[14,149],"detection":[15,73,150,205,230],"methods":[16,176],"often":[17],"overlook":[18],"the":[19,26,105,113,128,185],"data":[20,212],"scarcity":[21,213],"low-power":[23,218],"and":[25,44,86,147,173,180,194,214,249,263],"computational":[27,215],"constraints":[28,216],"IoT":[30,219],"gateways.":[31],"This":[32],"paper":[33],"addresses":[34],"these":[35],"challenges":[36],"by":[37],"first":[38],"analyzing":[39],"traffic":[40],"samples":[41,50,58],"with":[42,162,265],"t-SNE":[43],"Kernel":[45],"Density":[46],"Estimation,":[47],"revealing":[48],"that":[49,142,157],"from":[51,102],"other":[52],"exhibit":[54],"similarity":[55],"to":[56,97,107,192,236,245,257],"attack":[57],"a":[60,67,94,135,225],"target":[61,99,114],"device.":[62],"Based":[63],"on":[64,109],"this":[65],"observation,":[66],"novel":[68],"contrastive":[69,84,90,95,226,234],"learning-based":[70,227],"unsupervised":[71,228],"intrusion":[72,220,229],"method":[74],"named":[75],"CLU-ID":[76,158],"is":[77],"proposed,":[78],"which":[79],"operates":[80],"in":[81,217],"two":[82],"stages:":[83],"pre-training":[85,91,235],"centroid-centered":[87,124,243],"training.":[88],"stage":[92,126],"employs":[93],"loss":[96],"distinguish":[98],"device":[100,115,133],"flows":[101],"others,":[103],"directing":[104],"model":[106,182,186,246],"focus":[108],"distinctive":[110],"features":[111,119,146],"rather":[116],"than":[117],"common":[118],"shared":[120,179],"across":[121,177],"devices.":[122],"training":[125,164,244],"models":[127],"normal":[129,247],"distribution":[130,152],"each":[132],"within":[134],"single":[136],"neural":[137],"network,":[138],"generating":[139],"central":[140],"vectors":[141],"represent":[143],"typical":[144],"flow":[145],"enabling":[148],"as":[151,252],"outliers.":[153],"Experimental":[154],"results":[155],"demonstrate":[156],"achieves":[159],"state-of-the-art":[160],"performance":[161,259],"limited":[163],"samples,":[165],"outperforming":[166],"Autoencoder":[167],"(AE),":[168],"Deep":[169],"SVDD":[170],"(DSVDD),":[171],"Kitsune,":[172],"six":[174],"traditional":[175],"both":[178],"individual":[181],"scenarios.":[183],"Furthermore,":[184],"significantly":[187],"reduces":[188],"parameter":[189,268],"volume":[190],"compared":[191],"AE":[193,262],"DSVDD,":[195],"enhancing":[196],"suitability":[197],"for":[198,207],"resource-constrained":[199],"gateways,":[200],"while":[201],"also":[202],"exhibiting":[203],"effective":[204],"capability":[206],"unseen":[208],"attacks.":[209],"\u2022":[210,222,232,241,254],"Addresses":[211],"detection.":[221],"Proposes":[223],"CLU-ID,":[224],"method.":[231],"Employs":[233],"extract":[237],"device-specific":[238],"distinguishing":[239],"features.":[240],"Uses":[242],"distributions":[248],"detect":[250],"outliers":[251],"anomalies.":[253],"Achieves":[255],"up":[256],"30%":[258],"improvement":[260],"over":[261,266],"DSVDD":[264],"90%":[267],"reduction.":[269]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-03-12T06:13:28.667946","created_date":"2026-03-09T00:00:00"}
