{"id":"https://openalex.org/W7116719130","doi":"https://doi.org/10.23919/cnsm67658.2025.11297476","title":"XFAST: Efficient Feature Selection for High-Performance Network Traffic Analysis using eBPF/XDP and Genetic Algorithms","display_name":"XFAST: Efficient Feature Selection for High-Performance Network Traffic Analysis using eBPF/XDP and Genetic Algorithms","publication_year":2025,"publication_date":"2025-10-27","ids":{"openalex":"https://openalex.org/W7116719130","doi":"https://doi.org/10.23919/cnsm67658.2025.11297476"},"language":null,"primary_location":{"id":"doi:10.23919/cnsm67658.2025.11297476","is_oa":false,"landing_page_url":"https://doi.org/10.23919/cnsm67658.2025.11297476","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 21st International Conference on Network and Service Management (CNSM)","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/A5117345528","display_name":"Gustavo Henrique Ellwanger Einsfeldt","orcid":null},"institutions":[{"id":"https://openalex.org/I130442723","display_name":"Universidade Federal do Rio Grande do Sul","ror":"https://ror.org/041yk2d64","country_code":"BR","type":"education","lineage":["https://openalex.org/I130442723"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Gustavo Henrique Ellwanger Einsfeldt","raw_affiliation_strings":["Federal University of Rio Grande do Sul,Porto Alegre,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University of Rio Grande do Sul,Porto Alegre,Brazil","institution_ids":["https://openalex.org/I130442723"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013566686","display_name":"Schaeffer-Filho Alberto","orcid":null},"institutions":[{"id":"https://openalex.org/I130442723","display_name":"Universidade Federal do Rio Grande do Sul","ror":"https://ror.org/041yk2d64","country_code":"BR","type":"education","lineage":["https://openalex.org/I130442723"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Alberto E. Schaeffer-Filho","raw_affiliation_strings":["Federal University of Rio Grande do Sul,Porto Alegre,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University of Rio Grande do Sul,Porto Alegre,Brazil","institution_ids":["https://openalex.org/I130442723"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I130442723"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.52611247,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.6305000185966492,"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":0.6305000185966492,"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/T12326","display_name":"Network Packet Processing and Optimization","score":0.21960000693798065,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10714","display_name":"Software-Defined Networks and 5G","score":0.0640999972820282,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6743000149726868},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6075000166893005},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.5720000267028809},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5216000080108643},{"id":"https://openalex.org/keywords/fitness-function","display_name":"Fitness function","score":0.5077999830245972},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.486299991607666},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.47620001435279846},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4230000078678131},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4169999957084656},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4050000011920929}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7946000099182129},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6743000149726868},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6075000166893005},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5945000052452087},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.5720000267028809},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5216000080108643},{"id":"https://openalex.org/C176066374","wikidata":"https://www.wikidata.org/wiki/Q629118","display_name":"Fitness function","level":3,"score":0.5077999830245972},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.486299991607666},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.47620001435279846},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42890000343322754},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4230000078678131},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4169999957084656},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4050000011920929},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4027999937534332},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.38499999046325684},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3840999901294708},{"id":"https://openalex.org/C2781317605","wikidata":"https://www.wikidata.org/wiki/Q7832483","display_name":"Traffic analysis","level":2,"score":0.35899999737739563},{"id":"https://openalex.org/C2775941552","wikidata":"https://www.wikidata.org/wiki/Q25212305","display_name":"Isolation (microbiology)","level":2,"score":0.3515999913215637},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C81293917","wikidata":"https://www.wikidata.org/wiki/Q4189534","display_name":"System deployment","level":3,"score":0.32899999618530273},{"id":"https://openalex.org/C38822068","wikidata":"https://www.wikidata.org/wiki/Q131406","display_name":"Denial-of-service attack","level":3,"score":0.3287999927997589},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C182590292","wikidata":"https://www.wikidata.org/wiki/Q989632","display_name":"Network security","level":2,"score":0.32030001282691956},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.29789999127388},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C2778579508","wikidata":"https://www.wikidata.org/wiki/Q722192","display_name":"System call","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.28119999170303345},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C9899798","wikidata":"https://www.wikidata.org/wiki/Q629399","display_name":"Genetic operator","level":4,"score":0.25760000944137573},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/cnsm67658.2025.11297476","is_oa":false,"landing_page_url":"https://doi.org/10.23919/cnsm67658.2025.11297476","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 21st International Conference on Network and Service Management (CNSM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1967030981","https://openalex.org/W2023721289","https://openalex.org/W2038894244","https://openalex.org/W2053550965","https://openalex.org/W2079393414","https://openalex.org/W2087327261","https://openalex.org/W2101234009","https://openalex.org/W2130372754","https://openalex.org/W2158304283","https://openalex.org/W2167101736","https://openalex.org/W2245787472","https://openalex.org/W2296719434","https://openalex.org/W2469255646","https://openalex.org/W2512772081","https://openalex.org/W2789828921","https://openalex.org/W2798627392","https://openalex.org/W2903038868","https://openalex.org/W2978019044","https://openalex.org/W2979619501","https://openalex.org/W3011196476","https://openalex.org/W3164559343","https://openalex.org/W3196381982","https://openalex.org/W3198200730","https://openalex.org/W4293499229","https://openalex.org/W4296344149","https://openalex.org/W4311493355","https://openalex.org/W4402040227","https://openalex.org/W4409857519"],"related_works":[],"abstract_inverted_index":{"Intrusion":[0],"Detection":[1],"Systems":[2],"(IDS)":[3],"rely":[4],"heavily":[5],"on":[6],"feature-rich":[7],"data":[8],"and":[9,45,51,66,122,128,134],"dimensionality":[10],"reduction":[11],"to":[12,21,97,116],"identify":[13],"anomalies":[14],"in":[15,132],"high-throughput":[16],"networks.":[17],"However,":[18],"traditional":[19],"approaches":[20],"feature":[22,43,99],"selection":[23,46],"are":[24],"often":[25],"computationally":[26],"expensive":[27],"user-space":[28],"processes":[29],"that":[30,84,104],"limit":[31],"real-time":[32,58],"use.":[33],"In":[34],"this":[35],"paper,":[36],"we":[37],"present":[38],"XFAST,":[39],"a":[40,78,93],"novel":[41],"in-kernel":[42],"extraction":[44],"framework":[47],"built":[48],"using":[49,92],"eBPF/XDP":[50],"Genetic":[52],"Algorithms":[53],"(GA).":[54],"XFAST":[55,105],"enables":[56],"low-latency,":[57],"analysis":[59],"of":[60,109],"network":[61],"traffic":[62],"by":[63],"efficiently":[64],"computing":[65],"refining":[67],"flow-level":[68],"features":[69],"entirely":[70],"within":[71],"the":[72,86,107],"Linux":[73],"kernel.":[74],"Our":[75,101],"system":[76],"introduces":[77],"lightweight,":[79],"tail-call-based":[80],"GA":[81],"execution":[82],"model":[83,113],"distributes":[85],"evolutionary":[87],"process":[88],"across":[89],"multiple":[90],"packets,":[91],"hitbased":[94],"fitness":[95],"function":[96],"optimize":[98],"subsets.":[100],"evaluation":[102],"shows":[103],"improves":[106],"F1-score":[108],"an":[110],"Isolation":[111],"Forest":[112],"from":[114],"0.80":[115],"0.85":[117],"while":[118],"maintaining":[119],"negligible":[120],"CPU":[121],"memory":[123],"overhead,":[124],"demonstrating":[125],"its":[126],"scalability":[127],"efficiency":[129],"for":[130],"deployment":[131],"edge":[133],"cloud-native":[135],"environments.":[136]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-12-22T00:00:00"}
