{"id":"https://openalex.org/W7117891992","doi":"https://doi.org/10.1109/milcom64451.2025.11310014","title":"Detecting Stealthy Anomalies with Autoencoders Using Windowed Variance-Aware Loss Functions","display_name":"Detecting Stealthy Anomalies with Autoencoders Using Windowed Variance-Aware Loss Functions","publication_year":2025,"publication_date":"2025-10-06","ids":{"openalex":"https://openalex.org/W7117891992","doi":"https://doi.org/10.1109/milcom64451.2025.11310014"},"language":null,"primary_location":{"id":"doi:10.1109/milcom64451.2025.11310014","is_oa":false,"landing_page_url":"https://doi.org/10.1109/milcom64451.2025.11310014","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"MILCOM 2025 - 2025 IEEE Military Communications Conference (MILCOM)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5121702210","display_name":"Luke Hwang","orcid":null},"institutions":[{"id":"https://openalex.org/I94658018","display_name":"University of Memphis","ror":"https://ror.org/01cq23130","country_code":"US","type":"education","lineage":["https://openalex.org/I94658018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Luke Hwang","raw_affiliation_strings":["University of Memphis,Department of Computer Science,Memphis,TN,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Memphis,Department of Computer Science,Memphis,TN,USA","institution_ids":["https://openalex.org/I94658018"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121711605","display_name":"Md Nahidul Islam","orcid":null},"institutions":[{"id":"https://openalex.org/I94658018","display_name":"University of Memphis","ror":"https://ror.org/01cq23130","country_code":"US","type":"education","lineage":["https://openalex.org/I94658018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Md Nahidul Islam","raw_affiliation_strings":["University of Memphis,Department of Computer Science,Memphis,TN,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Memphis,Department of Computer Science,Memphis,TN,USA","institution_ids":["https://openalex.org/I94658018"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102061916","display_name":"Mohd Hasan Ali","orcid":null},"institutions":[{"id":"https://openalex.org/I94658018","display_name":"University of Memphis","ror":"https://ror.org/01cq23130","country_code":"US","type":"education","lineage":["https://openalex.org/I94658018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohd Hasan Ali","raw_affiliation_strings":["University of Memphis,Department of Electrical And Computer Engineering,Memphis,TN,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Memphis,Department of Electrical And Computer Engineering,Memphis,TN,USA","institution_ids":["https://openalex.org/I94658018"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111370894","display_name":"Dipankar Dasgupta","orcid":null},"institutions":[{"id":"https://openalex.org/I94658018","display_name":"University of Memphis","ror":"https://ror.org/01cq23130","country_code":"US","type":"education","lineage":["https://openalex.org/I94658018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dipankar Dasgupta","raw_affiliation_strings":["University of Memphis,Department of Computer Science,Memphis,TN,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Memphis,Department of Computer Science,Memphis,TN,USA","institution_ids":["https://openalex.org/I94658018"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051523220","display_name":"Myounggyu Won","orcid":"https://orcid.org/0000-0002-8703-8188"},"institutions":[{"id":"https://openalex.org/I94658018","display_name":"University of Memphis","ror":"https://ror.org/01cq23130","country_code":"US","type":"education","lineage":["https://openalex.org/I94658018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Myounggyu Won","raw_affiliation_strings":["University of Memphis,Department of Computer Science,Memphis,TN,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Memphis,Department of Computer Science,Memphis,TN,USA","institution_ids":["https://openalex.org/I94658018"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I94658018"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.55549365,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"981","last_page":"986"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.8507000207901001,"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":0.8507000207901001,"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":0.10000000149011612,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.008899999782443047,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.8267999887466431},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.6265000104904175},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5285000205039978},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.498199999332428},{"id":"https://openalex.org/keywords/intrusion","display_name":"Intrusion","score":0.42170000076293945},{"id":"https://openalex.org/keywords/skew","display_name":"Skew","score":0.34459999203681946},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3379000127315521}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8267999887466431},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6430000066757202},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.6265000104904175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5699999928474426},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5285000205039978},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.498199999332428},{"id":"https://openalex.org/C158251709","wikidata":"https://www.wikidata.org/wiki/Q354025","display_name":"Intrusion","level":2,"score":0.42170000076293945},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.34459999203681946},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3425999879837036},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3379000127315521},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.33230000734329224},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.3296999931335449},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2770000100135803},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C193519340","wikidata":"https://www.wikidata.org/wiki/Q891179","display_name":"Data loss","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C137524506","wikidata":"https://www.wikidata.org/wiki/Q2247688","display_name":"Anomaly-based intrusion detection system","level":3,"score":0.2599000036716461},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/milcom64451.2025.11310014","is_oa":false,"landing_page_url":"https://doi.org/10.1109/milcom64451.2025.11310014","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"MILCOM 2025 - 2025 IEEE Military Communications Conference (MILCOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1583975142","https://openalex.org/W2036887984","https://openalex.org/W2150847526","https://openalex.org/W2678934292","https://openalex.org/W2783741806","https://openalex.org/W2789828921","https://openalex.org/W2807786182","https://openalex.org/W2883554196","https://openalex.org/W2921871306","https://openalex.org/W2926701059","https://openalex.org/W2963197901","https://openalex.org/W2979046245","https://openalex.org/W3163301462","https://openalex.org/W4249837478","https://openalex.org/W4285227013","https://openalex.org/W4285233421","https://openalex.org/W4295788744","https://openalex.org/W4378365142","https://openalex.org/W4389513574","https://openalex.org/W4390499810","https://openalex.org/W4393150251","https://openalex.org/W4395041827","https://openalex.org/W4396605089","https://openalex.org/W4403150406","https://openalex.org/W4407135326","https://openalex.org/W4409801214"],"related_works":[],"abstract_inverted_index":{"Autoencoders":[0],"have":[1],"had":[2],"wide":[3],"research":[4],"appeal":[5],"in":[6,39],"cybersecurity":[7],"and":[8,19,60,96],"intrusion":[9,70,120],"detection":[10,71,121],"due":[11],"to":[12,31,68,83,125,133],"their":[13,20],"cutting":[14],"edge":[15],"pattern":[16],"recognition":[17],"capabilities":[18],"lack":[21],"of":[22,76,130],"reliance":[23],"on":[24,138],"labeled":[25],"data.":[26],"However,":[27],"autoencoders":[28],"often":[29],"struggle":[30],"detect":[32],"stealthy":[33],"anomalies":[34],"that":[35,78,115],"do":[36],"not":[37],"result":[38],"a":[40,134],"significantly":[41],"elevated":[42],"L1":[43,80,140],"reconstruction":[44,52],"loss.":[45,141],"In":[46],"this":[47],"paper,":[48],"we":[49],"introduce":[50],"novel":[51],"loss":[53,81,94],"functions\u2014Windowed":[54],"Standard":[55,63],"Deviation":[56,64],"Reconstruction":[57,65],"Loss":[58,66],"(WSR)":[59],"Windowed":[61],"Inverse":[62],"(WISR)\u2014designed":[67],"improve":[69],"accuracy":[72,122],"by":[73,123],"capturing":[74],"micro-patterns":[75],"instability":[77],"traditional":[79],"fails":[82],"identify.":[84],"We":[85],"also":[86],"present":[87],"an":[88],"autoencoder":[89,136],"model":[90],"integrated":[91],"with":[92],"these":[93],"functions":[95],"evaluate":[97],"its":[98],"performance":[99],"using":[100],"the":[101,116],"CIC-IDS2017":[102],"(Canadian":[103],"Institute":[104],"for":[105,127],"Cybersecurity":[106],"\u2013":[107],"Intrusion":[108],"Detection":[109],"System)":[110],"dataset.":[111],"Experimental":[112],"results":[113],"show":[114],"proposed":[117],"approach":[118],"improves":[119],"up":[124],"44%":[126],"different":[128],"types":[129],"attacks":[131],"compared":[132],"baseline":[135],"based":[137],"standard":[139]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-01-01T00:00:00"}