{"id":"https://openalex.org/W7083489649","doi":"https://doi.org/10.1186/s13635-025-00202-w","title":"Deep learning-based intrusion detection for IoT networks: a scalable and efficient approach","display_name":"Deep learning-based intrusion detection for IoT networks: a scalable and efficient approach","publication_year":2025,"publication_date":"2025-09-26","ids":{"openalex":"https://openalex.org/W7083489649","doi":"https://doi.org/10.1186/s13635-025-00202-w"},"language":"en","primary_location":{"id":"doi:10.1186/s13635-025-00202-w","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13635-025-00202-w","pdf_url":"https://jis-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13635-025-00202-w","source":{"id":"https://openalex.org/S5820498","display_name":"EURASIP Journal on Information Security","issn_l":"1687-4161","issn":["1687-4161","1687-417X","2510-523X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Information Security","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://jis-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13635-025-00202-w","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Md. Alamgir Hossain","orcid":"https://orcid.org/0000-0001-5120-2911"},"institutions":[{"id":"https://openalex.org/I138617543","display_name":"State University of Bangladesh","ror":"https://ror.org/01g14tq52","country_code":"BD","type":"education","lineage":["https://openalex.org/I138617543"]},{"id":"https://openalex.org/I4210112005","display_name":"Prime University","ror":"https://ror.org/022a1fs04","country_code":"BD","type":"education","lineage":["https://openalex.org/I4210112005"]}],"countries":["BD"],"is_corresponding":true,"raw_author_name":"Md. Alamgir Hossain","raw_affiliation_strings":["Department of Computer Science and Engineering, State University of Bangladesh, South Purbachal, Kanchan, Dhaka-1461, Bangladesh"],"raw_orcid":"https://orcid.org/0000-0001-5120-2911","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, State University of Bangladesh, South Purbachal, Kanchan, Dhaka-1461, Bangladesh","institution_ids":["https://openalex.org/I138617543","https://openalex.org/I4210112005"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I138617543","https://openalex.org/I4210112005"],"apc_list":{"value":1040,"currency":"USD","value_usd":1040},"apc_paid":{"value":1040,"currency":"USD","value_usd":1040},"fwci":34.3906,"has_fulltext":true,"cited_by_count":29,"citation_normalized_percentile":{"value":0.99711672,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"2025","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6899999976158142,"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"}},"topics":[{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6899999976158142,"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/T13067","display_name":"Geological Modeling and Analysis","score":0.029200000688433647,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14311","display_name":"Electrical and Electromagnetic Research","score":0.016300000250339508,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6980999708175659},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.6337000131607056},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5564000010490417},{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.5467000007629395},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5315999984741211},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4986000061035156},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4616999924182892}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.902400016784668},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6980999708175659},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.6337000131607056},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5564000010490417},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.5467000007629395},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5315999984741211},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5300999879837036},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4986000061035156},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4616999924182892},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4505000114440918},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.44940000772476196},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.365200012922287},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.36480000615119934},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3517000079154968},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34540000557899475},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3285999894142151},{"id":"https://openalex.org/C182590292","wikidata":"https://www.wikidata.org/wiki/Q989632","display_name":"Network security","level":2,"score":0.3167000114917755},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.2727000117301941},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2696000039577484},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.25369998812675476}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s13635-025-00202-w","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13635-025-00202-w","pdf_url":"https://jis-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13635-025-00202-w","source":{"id":"https://openalex.org/S5820498","display_name":"EURASIP Journal on Information Security","issn_l":"1687-4161","issn":["1687-4161","1687-417X","2510-523X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Information Security","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c6c8d1c852aa44ba92c60eca2d19232e","is_oa":true,"landing_page_url":"https://doaj.org/article/c6c8d1c852aa44ba92c60eca2d19232e","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":"EURASIP Journal on Information Security, Vol 2025, Iss 1, Pp 1-23 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s13635-025-00202-w","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13635-025-00202-w","pdf_url":"https://jis-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13635-025-00202-w","source":{"id":"https://openalex.org/S5820498","display_name":"EURASIP Journal on Information Security","issn_l":"1687-4161","issn":["1687-4161","1687-417X","2510-523X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Information Security","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7083489649.pdf","grobid_xml":"https://content.openalex.org/works/W7083489649.grobid-xml"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W2789828921","https://openalex.org/W2799758613","https://openalex.org/W3133664817","https://openalex.org/W3133886843","https://openalex.org/W3180273110","https://openalex.org/W3184668263","https://openalex.org/W3196738623","https://openalex.org/W3210577968","https://openalex.org/W4281693015","https://openalex.org/W4296010380","https://openalex.org/W4306176524","https://openalex.org/W4309173386","https://openalex.org/W4311421536","https://openalex.org/W4313654696","https://openalex.org/W4313891707","https://openalex.org/W4318826090","https://openalex.org/W4319316768","https://openalex.org/W4320004386","https://openalex.org/W4322722384","https://openalex.org/W4353057854","https://openalex.org/W4362705493","https://openalex.org/W4367309662","https://openalex.org/W4381568796","https://openalex.org/W4382281941","https://openalex.org/W4382793227","https://openalex.org/W4385368987","https://openalex.org/W4389236267","https://openalex.org/W4389477519","https://openalex.org/W4390561469","https://openalex.org/W4390913672","https://openalex.org/W4391224501","https://openalex.org/W4393115034","https://openalex.org/W4394712928","https://openalex.org/W4395000493","https://openalex.org/W4396746120","https://openalex.org/W4396938814","https://openalex.org/W4400475279","https://openalex.org/W4400602291","https://openalex.org/W4402023995","https://openalex.org/W4402306693","https://openalex.org/W4402883814","https://openalex.org/W4403191928","https://openalex.org/W4403212817","https://openalex.org/W4403840732","https://openalex.org/W4404002928","https://openalex.org/W4404468024","https://openalex.org/W4404946715","https://openalex.org/W4405601732","https://openalex.org/W4405778752","https://openalex.org/W4406704853","https://openalex.org/W6959792213"],"related_works":[],"abstract_inverted_index":{"The":[0,100,121,186,209],"rapid":[1],"expansion":[2],"of":[3,6,46,52,111,149,173,215],"the":[4,40,50,103,146,193,213,224],"Internet":[5],"Things":[7],"(IoT)":[8],"has":[9,18],"revolutionized":[10],"industries":[11],"by":[12,152],"enabling":[13],"seamless":[14],"connectivity,":[15],"but":[16],"it":[17],"also":[19],"introduced":[20],"significant":[21],"security":[22,57,221],"vulnerabilities,":[23],"making":[24],"IoT":[25,47,71,199,233],"networks":[26,81,90],"prime":[27],"targets":[28],"for":[29,66,197],"cyberattacks.":[30],"Traditional":[31],"intrusion":[32,98,200],"detection":[33,69],"systems":[34],"often":[35],"struggle":[36],"to":[37,96,134,212,226],"cope":[38],"with":[39,170],"high":[41],"volume":[42],"and":[43,55,92,118,126,138,157,178,183,206,217,228],"dynamic":[44],"nature":[45],"traffic,":[48],"necessitating":[49],"development":[51,214],"more":[53],"robust":[54],"intelligent":[56],"mechanisms.":[58],"This":[59],"research":[60],"presents":[61],"a":[62,108],"deep":[63,219],"learning-based":[64,220],"approach":[65],"real-time":[67,198],"threat":[68],"in":[70,203,232],"networks,":[72,87],"leveraging":[73],"advanced":[74],"models":[75,123],"such":[76],"as":[77],"1D":[78,143,164,190],"convolutional":[79],"neural":[80,89],"(CNNs),":[82],"long":[83],"short-term":[84],"memory":[85],"(LSTM)":[86],"recurrent":[88],"(RNNs),":[91],"multi-layer":[93],"perceptrons":[94],"(MLPs)":[95],"enhance":[97],"detection.":[99],"study":[101],"utilizes":[102],"CIC":[104],"IoT-DIAD":[105],"2024":[106],"dataset,":[107],"comprehensive":[109],"collection":[110],"flow-based":[112,129],"network":[113],"traffic":[114],"containing":[115],"both":[116],"benign":[117],"attack":[119,207],"scenarios.":[120],"proposed":[122],"were":[124],"trained":[125],"evaluated":[127],"on":[128],"feature":[130,204],"sets,":[131],"optimizing":[132],"hyperparameters":[133],"maximize":[135],"accuracy,":[136],"recall,":[137],"F1-score.":[139],"In":[140],"multi-class":[141],"classification,":[142],"CNN":[144,165,191],"achieved":[145,180],"highest":[147],"accuracy":[148,172],"99.12%,":[150],"followed":[151],"LSTM":[153],"(98.98%),":[154],"RNN":[155],"(98.43%),":[156],"MLP":[158,179],"(97.21%).":[159],"For":[160],"binary":[161],"anomaly":[162],"detection,":[163,201],"again":[166],"demonstrated":[167],"superior":[168],"performance":[169],"an":[171],"99.53%,":[174],"while":[175],"LSTM,":[176],"RNN,":[177],"99.52%,":[181],"99.25%,":[182],"98.78%,":[184],"respectively.":[185],"results":[187],"indicate":[188],"that":[189],"is":[192],"most":[194],"effective":[195],"model":[196],"excelling":[202],"extraction":[205],"classification.":[208],"findings":[210],"contribute":[211],"scalable":[216],"efficient":[218],"solutions,":[222],"improving":[223],"ability":[225],"detect":[227],"mitigate":[229],"cyber":[230],"threats":[231],"environments.":[234]},"counts_by_year":[{"year":2026,"cited_by_count":25},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}