{"id":"https://openalex.org/W4410057984","doi":"https://doi.org/10.32604/cmc.2025.064438","title":"ONTDAS: An Optimized Noise-Based Traffic Data Augmentation System for Generalizability Improvement of Traffic Classifiers","display_name":"ONTDAS: An Optimized Noise-Based Traffic Data Augmentation System for Generalizability Improvement of Traffic Classifiers","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4410057984","doi":"https://doi.org/10.32604/cmc.2025.064438"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.064438","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.064438","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.064438","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040960594","display_name":"Rongwei Yu","orcid":"https://orcid.org/0000-0002-1376-2720"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Rongwei Yu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014017635","display_name":"Jie Yin","orcid":"https://orcid.org/0000-0002-2063-8437"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jie Yin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113349836","display_name":"Jingyi Xiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jingyi Xiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101939710","display_name":"Qiyun Shao","orcid":"https://orcid.org/0009-0003-1458-3366"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiyun Shao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5056155495","display_name":"Lina Wang","orcid":"https://orcid.org/0000-0001-8085-1312"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lina Wang","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5040960594"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04658053,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"84","issue":"1","first_page":"365","last_page":"391"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9915000200271606,"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/T10320","display_name":"Neural Networks and Applications","score":0.9915000200271606,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9819999933242798,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9747999906539917,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/generalizability-theory","display_name":"Generalizability theory","score":0.8393993377685547},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5981049537658691},{"id":"https://openalex.org/keywords/traffic-noise","display_name":"Traffic noise","score":0.5630403757095337},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5330243110656738},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44192278385162354},{"id":"https://openalex.org/keywords/road-traffic","display_name":"Road traffic","score":0.41884058713912964},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3680174648761749},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3324242830276489},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.22370198369026184},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.19188475608825684},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1376289427280426},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11817538738250732},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.08897703886032104}],"concepts":[{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.8393993377685547},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5981049537658691},{"id":"https://openalex.org/C2781353297","wikidata":"https://www.wikidata.org/wiki/Q1748361","display_name":"Traffic noise","level":3,"score":0.5630403757095337},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5330243110656738},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44192278385162354},{"id":"https://openalex.org/C2985695025","wikidata":"https://www.wikidata.org/wiki/Q4323994","display_name":"Road traffic","level":2,"score":0.41884058713912964},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3680174648761749},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3324242830276489},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.22370198369026184},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.19188475608825684},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1376289427280426},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11817538738250732},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.08897703886032104},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.064438","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.064438","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.064438","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.064438","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2148143831","https://openalex.org/W2897202622","https://openalex.org/W2912386632","https://openalex.org/W3006050125","https://openalex.org/W3120227884","https://openalex.org/W3121886859","https://openalex.org/W3134226201","https://openalex.org/W3170039947","https://openalex.org/W3214335780","https://openalex.org/W4226076258","https://openalex.org/W4280500988","https://openalex.org/W4285043533","https://openalex.org/W4292974997","https://openalex.org/W4295789113","https://openalex.org/W4324007187","https://openalex.org/W4377091931","https://openalex.org/W4379054627","https://openalex.org/W4386694208","https://openalex.org/W4389416367","https://openalex.org/W4389951018","https://openalex.org/W4394564111","https://openalex.org/W4396696565"],"related_works":["https://openalex.org/W2006957861","https://openalex.org/W2181332251","https://openalex.org/W2186265465","https://openalex.org/W2008884673","https://openalex.org/W2419583548","https://openalex.org/W2043281224","https://openalex.org/W254641240","https://openalex.org/W2243312501","https://openalex.org/W2112047305","https://openalex.org/W1976325702"],"abstract_inverted_index":{"With":[0],"the":[1,13,43,87,103,135,156,164],"emergence":[2],"of":[3,37,45,118,158],"new":[4,38],"attack":[5],"techniques,":[6],"traffic":[7,61,105,136],"classifiers":[8,106,120],"usually":[9],"fail":[10],"to":[11,22,26,78,100,163],"maintain":[12],"expected":[14],"performance":[15,138,157],"in":[16],"real-world":[17],"network":[18],"environments.":[19],"In":[20],"order":[21],"have":[23],"sufficient":[24],"generalizability":[25,143],"deal":[27],"with":[28],"unknown":[29,145],"malicious":[30,146],"samples,":[31],"they":[32],"require":[33],"a":[34,69],"large":[35],"number":[36],"samples":[39,89],"for":[40,90],"retraining.":[41],"Considering":[42],"cost":[44],"data":[46,50,62,91,166],"collection":[47],"and":[48,73,121,139],"labeling,":[49],"augmentation":[51,63,167],"is":[52,84,98,160],"an":[53,58,74,94],"ideal":[54],"solution.":[55],"We":[56],"propose":[57],"optimized":[59,80],"noise-based":[60],"system,":[64],"ONTDAS.":[65],"The":[66,82,111,127,148],"system":[67,149],"uses":[68],"gradient-based":[70],"searching":[71],"algorithm":[72,97],"improved":[75,95],"Bayesian":[76],"optimizer":[77],"obtain":[79],"noise.":[81],"noise":[83],"injected":[85],"into":[86],"original":[88],"augmentation.":[92],"Then,":[93],"bagging":[96],"used":[99],"integrate":[101],"all":[102],"base":[104,119],"trained":[107],"on":[108,115,144],"noised":[109],"datasets.":[110],"experiments":[112],"verify":[113],"ONTDAS":[114,131,159],"6":[116],"types":[117],"4":[122],"publicly":[123],"available":[124],"datasets":[125],"respectively.":[126],"results":[128],"show":[129],"that":[130],"can":[132,150],"effectively":[133],"enhance":[134],"classifiers\u2019":[137],"significantly":[140,161],"improve":[141],"their":[142],"samples.":[147],"also":[151],"alleviate":[152],"dataset":[153],"imbalance.":[154],"Moreover,":[155],"superior":[162],"existing":[165],"methods":[168],"mentioned.":[169]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}