{"id":"https://openalex.org/W4406457928","doi":"https://doi.org/10.1109/bigdata62323.2024.10826127","title":"Adaptive Hierarchical GHSOM with Federated Learning for Context-Aware Anomaly Detection in IoT Networks","display_name":"Adaptive Hierarchical GHSOM with Federated Learning for Context-Aware Anomaly Detection in IoT Networks","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406457928","doi":"https://doi.org/10.1109/bigdata62323.2024.10826127"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10826127","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10826127","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5057307805","display_name":"Lulwah Alkulaib","orcid":"https://orcid.org/0000-0001-9827-0882"},"institutions":[{"id":"https://openalex.org/I36721946","display_name":"Kuwait University","ror":"https://ror.org/021e5j056","country_code":"KW","type":"education","lineage":["https://openalex.org/I36721946"]}],"countries":["KW"],"is_corresponding":true,"raw_author_name":"Lulwah Alkulaib","raw_affiliation_strings":["Kuwait University,Department of Computer Science,Kuwait"],"affiliations":[{"raw_affiliation_string":"Kuwait University,Department of Computer Science,Kuwait","institution_ids":["https://openalex.org/I36721946"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5057307805"],"corresponding_institution_ids":["https://openalex.org/I36721946"],"apc_list":null,"apc_paid":null,"fwci":1.461,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.84505948,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"5917","last_page":"5925"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9998000264167786,"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/T11498","display_name":"Security in Wireless Sensor Networks","score":0.998199999332428,"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/anomaly-detection","display_name":"Anomaly detection","score":0.8247921466827393},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.775934100151062},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6723358631134033},{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.6327602863311768},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3920831084251404},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.372453510761261},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.1431243121623993}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8247921466827393},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.775934100151062},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6723358631134033},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.6327602863311768},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3920831084251404},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.372453510761261},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.1431243121623993},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10826127","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10826127","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1967561194","https://openalex.org/W1990517717","https://openalex.org/W2072641892","https://openalex.org/W2118209142","https://openalex.org/W2119046642","https://openalex.org/W2122646361","https://openalex.org/W2278186031","https://openalex.org/W2605800822","https://openalex.org/W2768947629","https://openalex.org/W2887017691","https://openalex.org/W2963318081","https://openalex.org/W2995022099","https://openalex.org/W3021654819","https://openalex.org/W3085955590","https://openalex.org/W6728757088"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"This":[0,112,213],"paper":[1],"proposes":[2],"an":[3],"adaptive":[4,106,220],"hierarchical":[5],"Growing":[6],"Hierarchical":[7],"Self-Organizing":[8],"Map":[9],"(GHSOM)":[10],"integrated":[11],"with":[12,78,162,203,223],"Federated":[13],"Learning":[14],"(FL)":[15],"for":[16,236],"scalable":[17],"and":[18,62,120,132,183,230],"privacy-preserving":[19,80],"anomaly":[20,43,155,232],"detection":[21,44,156,233],"in":[22],"distributed":[23],"Internet":[24],"of":[25,34,76,82,122,150,218],"Things":[26],"(IoT)":[27],"networks.":[28],"In":[29],"IoT":[30,193,238],"environments,":[31],"vast":[32],"amounts":[33],"data":[35,58],"are":[36,93],"generated":[37],"from":[38],"heterogeneous":[39],"sensors,":[40],"requiring":[41],"robust":[42],"mechanisms":[45],"to":[46,57,146,200,208,226],"ensure":[47],"system":[48,190],"reliability.":[49],"Traditional":[50],"centralized":[51],"approaches":[52],"face":[53],"significant":[54,169],"challenges":[55],"related":[56],"privacy,":[59],"communication":[60,184,205],"overhead,":[61],"scalability.":[63],"The":[64],"proposed":[65,189],"model":[66,87,140,170],"addresses":[67],"these":[68],"issues":[69],"by":[70,172],"combining":[71,219],"the":[72,79,96,109,118,123,138,147,177,188,216],"local":[73],"processing":[74],"power":[75],"GHSOM":[77,110,139,211,221],"capabilities":[81],"FL,":[83],"ensuring":[84],"that":[85],"only":[86,168],"updates,":[88],"rather":[89],"than":[90],"raw":[91],"data,":[92,126],"shared":[94,175],"across":[95,176],"network.":[97],"To":[98],"further":[99,179],"enhance":[100],"efficiency,":[101],"we":[102],"introduce":[103],"a":[104,163],"novel":[105],"hierarchy":[107,113],"within":[108],"structure.":[111],"dynamically":[114],"adjusts":[115],"based":[116],"on":[117,191],"context":[119],"characteristics":[121],"incoming":[124],"sensor":[125,152,194],"such":[127],"as":[128],"its":[129,143,197],"variance,":[130],"frequency,":[131],"criticality.":[133],"By":[134],"embedding":[135],"this":[136],"context-awareness,":[137],"can":[141],"tailor":[142],"clustering":[144],"process":[145],"specific":[148],"requirements":[149],"each":[151],"node,":[153],"improving":[154],"accuracy.":[157],"Additionally,":[158],"FL":[159],"is":[160],"enhanced":[161],"selective":[164],"update":[165],"mechanism,":[166],"where":[167],"updates\u2014triggered":[171],"detected":[173],"anomalies\u2014are":[174],"network,":[178],"reducing":[180],"bandwidth":[181],"usage":[182],"costs.":[185],"We":[186],"validate":[187],"diverse":[192],"datasets,":[195],"demonstrating":[196],"superior":[198],"ability":[199],"detect":[201],"anomalies":[202],"reduced":[204],"overhead":[206],"compared":[207],"non-adaptive":[209],"federated":[210,224],"approaches.":[212],"work":[214],"highlights":[215],"potential":[217],"hierarchies":[222],"learning":[225],"create":[227],"scalable,":[228],"privacy-preserving,":[229],"efficient":[231],"systems":[234],"suitable":[235],"real-time":[237],"applications.":[239]},"counts_by_year":[{"year":2025,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
