{"id":"https://openalex.org/W4401214642","doi":"https://doi.org/10.1145/3638530.3654360","title":"AD-NEv++ - The multi-architecture neuroevolution-based multivariate anomaly detection framework","display_name":"AD-NEv++ - The multi-architecture neuroevolution-based multivariate anomaly detection framework","publication_year":2024,"publication_date":"2024-07-14","ids":{"openalex":"https://openalex.org/W4401214642","doi":"https://doi.org/10.1145/3638530.3654360"},"language":"en","primary_location":{"id":"doi:10.1145/3638530.3654360","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3638530.3654360","pdf_url":null,"source":{"id":"https://openalex.org/S4363608771","display_name":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","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/A5036847074","display_name":"Marcin Pietro\u0144","orcid":"https://orcid.org/0000-0001-9357-9231"},"institutions":[{"id":"https://openalex.org/I126596746","display_name":"Jagiellonian University","ror":"https://ror.org/03bqmcz70","country_code":"PL","type":"education","lineage":["https://openalex.org/I126596746"]},{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Marcin Pietro\u0144","raw_affiliation_strings":["AGH University of Krak\u00f3w, Krak\u00f3w, Poland"],"raw_orcid":"https://orcid.org/0000-0001-9357-9231","affiliations":[{"raw_affiliation_string":"AGH University of Krak\u00f3w, Krak\u00f3w, Poland","institution_ids":["https://openalex.org/I126596746","https://openalex.org/I686019"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084220744","display_name":"Dominik \u017burek","orcid":"https://orcid.org/0000-0001-5329-1452"},"institutions":[{"id":"https://openalex.org/I126596746","display_name":"Jagiellonian University","ror":"https://ror.org/03bqmcz70","country_code":"PL","type":"education","lineage":["https://openalex.org/I126596746"]},{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Dominik \u017burek","raw_affiliation_strings":["AGH University of Krak\u00f3w, Krak\u00f3w, Poland"],"raw_orcid":"https://orcid.org/0000-0001-5329-1452","affiliations":[{"raw_affiliation_string":"AGH University of Krak\u00f3w, Krak\u00f3w, Poland","institution_ids":["https://openalex.org/I126596746","https://openalex.org/I686019"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008887467","display_name":"Kamil Faber","orcid":"https://orcid.org/0000-0003-4221-0017"},"institutions":[{"id":"https://openalex.org/I126596746","display_name":"Jagiellonian University","ror":"https://ror.org/03bqmcz70","country_code":"PL","type":"education","lineage":["https://openalex.org/I126596746"]},{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Kamil Faber","raw_affiliation_strings":["AGH University of Krak\u00f3w, Krak\u00f3w, Poland"],"raw_orcid":"https://orcid.org/0000-0003-4221-0017","affiliations":[{"raw_affiliation_string":"AGH University of Krak\u00f3w, Krak\u00f3w, Poland","institution_ids":["https://openalex.org/I126596746","https://openalex.org/I686019"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111365840","display_name":"Anna W\u00f3jcik","orcid":"https://orcid.org/0009-0009-6910-5621"},"institutions":[{"id":"https://openalex.org/I126596746","display_name":"Jagiellonian University","ror":"https://ror.org/03bqmcz70","country_code":"PL","type":"education","lineage":["https://openalex.org/I126596746"]},{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Anna W\u00f3jcik","raw_affiliation_strings":["AGH University of Krak\u00f3w, Krak\u00f3w, Poland"],"raw_orcid":"https://orcid.org/0009-0009-6910-5621","affiliations":[{"raw_affiliation_string":"AGH University of Krak\u00f3w, Krak\u00f3w, Poland","institution_ids":["https://openalex.org/I126596746","https://openalex.org/I686019"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010914442","display_name":"Roberto Corizzo","orcid":"https://orcid.org/0000-0001-8366-6059"},"institutions":[{"id":"https://openalex.org/I181401687","display_name":"American University","ror":"https://ror.org/052w4zt36","country_code":"US","type":"education","lineage":["https://openalex.org/I181401687"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Roberto Corizzo","raw_affiliation_strings":["American University in Washington, Washington, Poland"],"raw_orcid":"https://orcid.org/0000-0001-8366-6059","affiliations":[{"raw_affiliation_string":"American University in Washington, Washington, Poland","institution_ids":["https://openalex.org/I181401687"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"607","last_page":"610"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9973999857902527,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9828000068664551,"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/neuroevolution","display_name":"Neuroevolution","score":0.7585116624832153},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6696973443031311},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.6620556116104126},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5576462745666504},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5380046367645264},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.5364001989364624},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4379289150238037},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.18060460686683655},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.15716755390167236},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07725796103477478}],"concepts":[{"id":"https://openalex.org/C118070581","wikidata":"https://www.wikidata.org/wiki/Q2060528","display_name":"Neuroevolution","level":3,"score":0.7585116624832153},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6696973443031311},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6620556116104126},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5576462745666504},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5380046367645264},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.5364001989364624},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4379289150238037},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.18060460686683655},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.15716755390167236},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07725796103477478},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3638530.3654360","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3638530.3654360","pdf_url":null,"source":{"id":"https://openalex.org/S4363608771","display_name":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","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":5,"referenced_works":["https://openalex.org/W2111935653","https://openalex.org/W2531563875","https://openalex.org/W4285299229","https://openalex.org/W4319300976","https://openalex.org/W4324056437"],"related_works":["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/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4377864969","https://openalex.org/W2972971679"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,151,193],"tools":[2],"and":[3,12,35,43,63,78,80,86,106,136,154,167,180],"methods":[4],"enable":[5],"key":[6],"analytical":[7],"capabilities":[8],"in":[9,19,121,190],"modern":[10],"cyberphysical":[11],"sensor-based":[13],"systems.":[14],"Despite":[15],"the":[16,111,118,122,157,182],"fast-paced":[17],"development":[18],"deep":[20,164],"learning":[21,56,165],"architectures":[22,79,166],"for":[23,28,55,75,170],"anomaly":[24,150,171,192],"detection,":[25],"model":[26,104,188],"optimization":[27],"a":[29,33,50,95,127],"given":[30],"dataset":[31],"is":[32],"cumbersome":[34],"time-consuming":[36],"process.":[37],"Neuroevolution":[38],"could":[39],"be":[40],"an":[41],"effective":[42],"efficient":[44],"solution":[45],"to":[46,84],"this":[47,90],"problem,":[48],"as":[49],"fully":[51],"automated":[52],"search":[53],"method":[54,98,109],"optimal":[57],"neural":[58,131],"networks,":[59],"supporting":[60,126],"both":[61],"gradient":[62],"non-gradient":[64],"fine":[65],"tuning.":[66],"However,":[67],"existing":[68,114],"frameworks":[69],"incorporating":[70],"neuroevolution":[71,123],"lack":[72],"of":[73,113,130],"support":[74],"new":[76],"layers":[77],"are":[81],"typically":[82],"limited":[83],"convolutional":[85,138],"LSTM":[87],"layers.":[88,139],"In":[89],"paper":[91],"we":[92],"propose":[93],"AD-NEv++,":[94],"three-stage":[96],"neuroevolution-based":[97,168],"that":[99,156,176],"synergically":[100],"combines":[101],"subspace":[102],"evolution,":[103,105],"fine-tuning.":[107],"Our":[108,140],"overcomes":[110],"limitations":[112],"approaches":[115,169],"by":[116,160],"optimizing":[117],"mutation":[119],"operator":[120],"process,":[124],"while":[125],"wide":[128],"spectrum":[129],"layers,":[132],"including":[133],"attention,":[134],"dense,":[135],"graph":[137],"extensive":[141],"experimental":[142],"evaluation":[143],"was":[144],"conducted":[145],"with":[146],"widely":[147],"adopted":[148],"multivariate":[149],"benchmark":[152],"datasets,":[153],"showed":[155],"models":[158],"generated":[159],"AD-NEv++":[161,177],"outperform":[162,181],"well-known":[163],"detection.":[172],"Moreover,":[173],"results":[174],"show":[175],"can":[178],"improve":[179],"state-of-the-art":[183],"GNN":[184],"(Graph":[185],"Neural":[186],"Networks)":[187],"architecture":[189],"all":[191],"benchmarks.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}