{"id":"https://openalex.org/W7139122205","doi":"https://doi.org/10.1109/globecom59602.2025.11431798","title":"One Model to Catch Them All: Autoencoder-Based Anomaly Detection in Next-Generation Networks","display_name":"One Model to Catch Them All: Autoencoder-Based Anomaly Detection in Next-Generation Networks","publication_year":2025,"publication_date":"2025-12-08","ids":{"openalex":"https://openalex.org/W7139122205","doi":"https://doi.org/10.1109/globecom59602.2025.11431798"},"language":null,"primary_location":{"id":"doi:10.1109/globecom59602.2025.11431798","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom59602.2025.11431798","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2025 - 2025 IEEE Global Communications Conference","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/A5005612783","display_name":"Islam Obaidat","orcid":"https://orcid.org/0000-0002-2258-0785"},"institutions":[{"id":"https://openalex.org/I35777872","display_name":"North Carolina Agricultural and Technical State University","ror":"https://ror.org/02aze4h65","country_code":"US","type":"education","lineage":["https://openalex.org/I35777872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Islam Obaidat","raw_affiliation_strings":["North Carolina A&T State University,Department of CST,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North Carolina A&T State University,Department of CST,USA","institution_ids":["https://openalex.org/I35777872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058941449","display_name":"Furqan Rustam","orcid":"https://orcid.org/0000-0001-8403-1047"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Furqan Rustam","raw_affiliation_strings":["University College,School of CS,Dublin,Ireland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University College,School of CS,Dublin,Ireland","institution_ids":["https://openalex.org/I100930933"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022546955","display_name":"Anca Delia Jurcut","orcid":"https://orcid.org/0000-0002-2705-1823"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Anca Delia Jurcut","raw_affiliation_strings":["University College,School of CS,Dublin,Ireland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University College,School of CS,Dublin,Ireland","institution_ids":["https://openalex.org/I100930933"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.7149055,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3323","last_page":"3328"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.781000018119812,"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":0.781000018119812,"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":0.10499999672174454,"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":0.027699999511241913,"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":0.8126000165939331},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7175999879837036},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.5565000176429749},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5504000186920166},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.47440001368522644},{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.4207000136375427},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.39640000462532043},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.3549000024795532},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.34209999442100525}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8126000165939331},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7175999879837036},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6851000189781189},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.5565000176429749},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5504000186920166},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5128999948501587},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4772000014781952},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.47440001368522644},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.4207000136375427},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4018999934196472},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.39640000462532043},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3549000024795532},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.337799996137619},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C169988225","wikidata":"https://www.wikidata.org/wiki/Q7832484","display_name":"Traffic classification","level":3,"score":0.26510000228881836},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C2781317605","wikidata":"https://www.wikidata.org/wiki/Q7832483","display_name":"Traffic analysis","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.25870001316070557},{"id":"https://openalex.org/C182590292","wikidata":"https://www.wikidata.org/wiki/Q989632","display_name":"Network security","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom59602.2025.11431798","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom59602.2025.11431798","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2025 - 2025 IEEE Global Communications Conference","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":19,"referenced_works":["https://openalex.org/W2079735306","https://openalex.org/W2100495367","https://openalex.org/W2136655611","https://openalex.org/W2194775991","https://openalex.org/W2982682021","https://openalex.org/W3111118183","https://openalex.org/W3174500793","https://openalex.org/W4322753725","https://openalex.org/W4361289962","https://openalex.org/W4386413091","https://openalex.org/W4387953240","https://openalex.org/W4391514872","https://openalex.org/W4392353733","https://openalex.org/W4400131985","https://openalex.org/W4401990418","https://openalex.org/W4401990795","https://openalex.org/W4402159181","https://openalex.org/W4403231530","https://openalex.org/W4407667328"],"related_works":[],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,112],"is":[2,73,117,120,171,184],"crucial":[3],"for":[4,110],"ensuring":[5,176],"the":[6,174,192],"security":[7],"and":[8,22,39,62,131,158,190],"reliability":[9],"of":[10,18],"next-generation":[11,55,114],"heterogeneous":[12],"networks,":[13],"which":[14],"integrate":[15],"a":[16,86,104,123],"variety":[17],"devices":[19],"(e.g.,":[20,90],"IoT":[21,61,130,157],"traditional":[23,63,132,159],"devices).":[24],"Autoencoder":[25,106],"(AE)-based":[26],"approaches":[27],"have":[28],"shown":[29],"promise":[30],"in":[31,48,54,97,113,142,195,208],"identifying":[32],"network":[33],"anomalies":[34,95],"by":[35],"modeling":[36],"benign":[37,126],"traffic":[38,68,88,160,189],"detecting":[40,209],"deviations.":[41],"However,":[42],"existing":[43],"AE":[44,81],"methods,":[45],"mostly":[46],"evaluated":[47],"homogeneous":[49],"environments,":[50],"struggle":[51],"to":[52,66,93,136,161,178,186,205],"generalize":[53],"multi-environment":[56],"(M-En)":[57],"networks":[58,116],"(comprising":[59],"both":[60,129],"devices)":[64],"due":[65],"diverse":[67],"patterns.":[69],"This":[70],"generalization":[71],"issue":[72],"evidenced":[74],"through":[75],"an":[76],"initial":[77],"ablation":[78],"study,":[79],"where":[80],"models":[82],"trained":[83,121],"solely":[84],"on":[85,122],"single":[87],"type":[89],"IoT-only)":[91],"fail":[92],"detect":[94],"effectively":[96],"M-En":[98,115,143,188],"networks.":[99,144],"To":[100],"address":[101],"this":[102],"limitation,":[103],"Residual":[105],"(RD-AE)":[107],"specifically":[108],"designed":[109],"anomaly":[111],"presented.":[118],"RD-AE":[119,202],"combined":[124],"actual":[125],"dataset":[127],"containing":[128],"traffic,":[133],"allowing":[134],"it":[135],"accurately":[137],"identify":[138],"abnormal":[139],"(malicious)":[140],"deviations":[141],"An":[145],"attention-based":[146],"BiLSTM":[147],"classifier":[148],"subsequently":[149],"categorizes":[150],"these":[151],"detected":[152],"anomalies,":[153],"differentiating":[154],"between":[155],"malicious":[156],"support":[162],"targeted":[163],"defensive":[164],"measures.":[165],"A":[166,181],"human-in-the-loop":[167],"continuous":[168],"learning":[169],"mechanism":[170],"integrated":[172],"into":[173],"classifier,":[175],"adaptability":[177],"emerging":[179],"threats.":[180],"realistic":[182],"testbed":[183],"used":[185],"generate":[187],"evaluate":[191],"proposed":[193],"framework":[194],"real":[196],"time.":[197],"Experimental":[198],"results":[199],"show":[200],"that":[201],"achieves":[203],"up":[204],"97.4%":[206],"accuracy":[207],"previously":[210],"unseen":[211],"attack":[212],"scenarios.":[213]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-20T00:00:00"}