{"id":"https://openalex.org/W4413008541","doi":"https://doi.org/10.3390/make7030078","title":"AE-DTNN: Autoencoder\u2013Dense\u2013Transformer Neural Network Model for Efficient Anomaly-Based Intrusion Detection Systems","display_name":"AE-DTNN: Autoencoder\u2013Dense\u2013Transformer Neural Network Model for Efficient Anomaly-Based Intrusion Detection Systems","publication_year":2025,"publication_date":"2025-08-06","ids":{"openalex":"https://openalex.org/W4413008541","doi":"https://doi.org/10.3390/make7030078"},"language":"en","primary_location":{"id":"doi:10.3390/make7030078","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7030078","pdf_url":"https://www.mdpi.com/2504-4990/7/3/78/pdf?version=1754492009","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"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":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/7/3/78/pdf?version=1754492009","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101177635","display_name":"Hesham Kamal","orcid":"https://orcid.org/0009-0009-1042-7503"},"institutions":[{"id":"https://openalex.org/I96823368","display_name":"German University in Cairo","ror":"https://ror.org/03rjt0z37","country_code":"EG","type":"education","lineage":["https://openalex.org/I96823368"]}],"countries":["EG"],"is_corresponding":true,"raw_author_name":"Hesham Kamal","raw_affiliation_strings":["Networks Department, Faculty of Information Engineering and Technology (IET), German University in Cairo (GUC), New Cairo 11835, Egypt"],"raw_orcid":"https://orcid.org/0009-0009-1042-7503","affiliations":[{"raw_affiliation_string":"Networks Department, Faculty of Information Engineering and Technology (IET), German University in Cairo (GUC), New Cairo 11835, Egypt","institution_ids":["https://openalex.org/I96823368"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090276836","display_name":"Maggie Mashaly","orcid":"https://orcid.org/0000-0002-8313-5554"},"institutions":[{"id":"https://openalex.org/I96823368","display_name":"German University in Cairo","ror":"https://ror.org/03rjt0z37","country_code":"EG","type":"education","lineage":["https://openalex.org/I96823368"]}],"countries":["EG"],"is_corresponding":true,"raw_author_name":"Maggie Mashaly","raw_affiliation_strings":["Networks Department, Faculty of Information Engineering and Technology (IET), German University in Cairo (GUC), New Cairo 11835, Egypt"],"raw_orcid":"https://orcid.org/0000-0002-8313-5554","affiliations":[{"raw_affiliation_string":"Networks Department, Faculty of Information Engineering and Technology (IET), German University in Cairo (GUC), New Cairo 11835, Egypt","institution_ids":["https://openalex.org/I96823368"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5090276836","https://openalex.org/A5101177635"],"corresponding_institution_ids":["https://openalex.org/I96823368"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":9.0898,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.97810059,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":100},"biblio":{"volume":"7","issue":"3","first_page":"78","last_page":"78"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":1.0,"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":1.0,"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":0.9988999962806702,"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":0.9973000288009644,"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":0.8700440526008606},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.651934802532196},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5969843864440918},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5757941603660583},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5702612400054932},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.4663962721824646},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45804330706596375},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4186760187149048},{"id":"https://openalex.org/keywords/intrusion","display_name":"Intrusion","score":0.41409528255462646},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3577594757080078},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.2322579324245453},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18547677993774414},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.09951359033584595},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09733381867408752}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8700440526008606},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.651934802532196},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5969843864440918},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5757941603660583},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5702612400054932},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.4663962721824646},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45804330706596375},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4186760187149048},{"id":"https://openalex.org/C158251709","wikidata":"https://www.wikidata.org/wiki/Q354025","display_name":"Intrusion","level":2,"score":0.41409528255462646},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3577594757080078},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.2322579324245453},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18547677993774414},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.09951359033584595},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09733381867408752},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C17409809","wikidata":"https://www.wikidata.org/wiki/Q161764","display_name":"Geochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/make7030078","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7030078","pdf_url":"https://www.mdpi.com/2504-4990/7/3/78/pdf?version=1754492009","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"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":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:3f701606ff1140e8833b95f3dfa6996f","is_oa":true,"landing_page_url":"https://doaj.org/article/3f701606ff1140e8833b95f3dfa6996f","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":"Machine Learning and Knowledge Extraction, Vol 7, Iss 3, p 78 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/make7030078","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7030078","pdf_url":"https://www.mdpi.com/2504-4990/7/3/78/pdf?version=1754492009","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"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":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4413008541.pdf","grobid_xml":"https://content.openalex.org/works/W4413008541.grobid-xml"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W1495172800","https://openalex.org/W1576462183","https://openalex.org/W2099940443","https://openalex.org/W2105442001","https://openalex.org/W2112796928","https://openalex.org/W2139731313","https://openalex.org/W2150847526","https://openalex.org/W2883657838","https://openalex.org/W2909444216","https://openalex.org/W2949528415","https://openalex.org/W2950916798","https://openalex.org/W3126752450","https://openalex.org/W3211805421","https://openalex.org/W4206101301","https://openalex.org/W4225492836","https://openalex.org/W4226021186","https://openalex.org/W4286367234","https://openalex.org/W4293414946","https://openalex.org/W4303022566","https://openalex.org/W4313345743","https://openalex.org/W4313397661","https://openalex.org/W4321240473","https://openalex.org/W4321377727","https://openalex.org/W4324046881","https://openalex.org/W4366150177","https://openalex.org/W4377193656","https://openalex.org/W4382046320","https://openalex.org/W4385245566","https://openalex.org/W4385416291","https://openalex.org/W4388969448","https://openalex.org/W4390193214","https://openalex.org/W4391288972","https://openalex.org/W4392029839","https://openalex.org/W4392694748","https://openalex.org/W4396792110","https://openalex.org/W4400036903","https://openalex.org/W4404629055","https://openalex.org/W4405722007","https://openalex.org/W4406907824","https://openalex.org/W4407842019","https://openalex.org/W4408150693","https://openalex.org/W4411359742","https://openalex.org/W4412628677","https://openalex.org/W6739901393","https://openalex.org/W6840391774","https://openalex.org/W6878182833"],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W4363671829","https://openalex.org/W2780476542","https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2667207928"],"abstract_inverted_index":{"In":[0,105],"this":[1],"study,":[2],"we":[3],"introduce":[4],"an":[5,15],"enhanced":[6],"hybrid":[7],"Autoencoder\u2013Dense\u2013Transformer":[8],"Neural":[9],"Network":[10],"(AE-DTNN)":[11],"model":[12,134,148,173],"for":[13,79,138,143],"developing":[14],"effective":[16],"intrusion":[17],"detection":[18,30],"system":[19],"(IDS)":[20],"aimed":[21],"at":[22],"improving":[23],"the":[24,88,95,109,112,130,133,147,161,167,171],"performance":[25],"and":[26,36,69,82,94,124,141,151,156],"robustness":[27],"of":[28,51,97,170,188],"threat":[29],"strategies":[31],"within":[32],"a":[33,49,185],"rapidly":[34],"changing":[35],"increasingly":[37],"complex":[38],"network":[39,45,64,189],"landscape.":[40],"The":[41,62],"Autoencoder":[42],"component":[43],"restructures":[44],"traffic":[46],"data,":[47],"while":[48],"stack":[50],"Dense":[52],"layers":[53],"performs":[54],"feature":[55],"extraction":[56],"to":[57,100,176,183],"generate":[58],"more":[59],"meaningful":[60],"representations.":[61],"Transformer":[63],"then":[65],"facilitates":[66],"highly":[67],"precise":[68],"comprehensive":[70],"classification.":[71,128,145],"Our":[72],"strategy":[73],"incorporates":[74],"adaptive":[75],"synthetic":[76],"sampling":[77],"(ADASYN)":[78],"both":[80],"binary":[81,122,139,155],"multi-class":[83,127,144,157],"classification":[84,123,140],"tasks,":[85],"complemented":[86],"by":[87],"edited":[89],"nearest":[90],"neighbors":[91],"(ENN)":[92],"technique":[93],"use":[96],"class":[98,102],"weights":[99],"mitigate":[101],"imbalance":[103],"issues.":[104],"experiments":[106],"conducted":[107],"on":[108,160],"NF-BoT-IoT-v2":[110],"dataset,":[111,132],"AE-DTNN-based":[113],"IDS":[114],"achieved":[115],"outstanding":[116],"performance,":[117],"with":[118,191],"99.98%":[119],"accuracy":[120,137,153],"in":[121,126,154,174],"98.30%":[125],"On":[129],"NSL-KDD":[131],"reached":[135],"98.57%":[136],"97.50%":[142],"Additionally,":[146],"attained":[149],"99.92%":[150],"99.78%":[152],"classification,":[158],"respectively,":[159],"CSE-CIC-IDS2018":[162],"dataset.":[163],"These":[164],"results":[165],"demonstrate":[166],"exceptional":[168],"effectiveness":[169],"proposed":[172],"contrast":[175],"conventional":[177],"approaches,":[178],"highlighting":[179],"its":[180],"strong":[181],"potential":[182],"detect":[184],"broad":[186],"range":[187],"intrusions":[190],"high":[192],"reliability.":[193]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-05T09:01:59.212387","created_date":"2025-10-10T00:00:00"}
