{"id":"https://openalex.org/W2783366320","doi":"https://doi.org/10.23919/cnsm.2017.8255998","title":"GML learning, a generic machine learning model for network measurements analysis","display_name":"GML learning, a generic machine learning model for network measurements analysis","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2783366320","doi":"https://doi.org/10.23919/cnsm.2017.8255998","mag":"2783366320"},"language":"en","primary_location":{"id":"doi:10.23919/cnsm.2017.8255998","is_oa":false,"landing_page_url":"https://doi.org/10.23919/cnsm.2017.8255998","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Network and Service Management (CNSM)","raw_type":"proceedings-article"},"type":"article","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/A5081556750","display_name":"Pedro Casas","orcid":"https://orcid.org/0000-0002-0951-2331"},"institutions":[{"id":"https://openalex.org/I132118926","display_name":"Austrian Institute of Technology","ror":"https://ror.org/04knbh022","country_code":"AT","type":"facility","lineage":["https://openalex.org/I132118926"]}],"countries":["AT"],"is_corresponding":true,"raw_author_name":"Pedro Casas","raw_affiliation_strings":["AIT Austrian Institute of Technology"],"affiliations":[{"raw_affiliation_string":"AIT Austrian Institute of Technology","institution_ids":["https://openalex.org/I132118926"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017530794","display_name":"Juan Vanerio","orcid":"https://orcid.org/0000-0003-3120-5028"},"institutions":[{"id":"https://openalex.org/I4210166370","display_name":"Universidad La Rep\u00fablica","ror":"https://ror.org/05xs36f43","country_code":"CL","type":"education","lineage":["https://openalex.org/I4210166370"]},{"id":"https://openalex.org/I180910786","display_name":"Universidad de la Rep\u00fablica","ror":"https://ror.org/030bbe882","country_code":"UY","type":"education","lineage":["https://openalex.org/I180910786"]}],"countries":["CL","UY"],"is_corresponding":false,"raw_author_name":"Juan Vanerio","raw_affiliation_strings":["Universidad de la Rep\u00fablica"],"affiliations":[{"raw_affiliation_string":"Universidad de la Rep\u00fablica","institution_ids":["https://openalex.org/I4210166370","https://openalex.org/I180910786"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101814201","display_name":"Kensuke Fukuda","orcid":"https://orcid.org/0000-0001-8372-2807"},"institutions":[{"id":"https://openalex.org/I184597095","display_name":"National Institute of Informatics","ror":"https://ror.org/04ksd4g47","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1319490839","https://openalex.org/I184597095","https://openalex.org/I4210158934"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kensuke Fukuda","raw_affiliation_strings":["National Institute of Informatics"],"affiliations":[{"raw_affiliation_string":"National Institute of Informatics","institution_ids":["https://openalex.org/I184597095"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5081556750"],"corresponding_institution_ids":["https://openalex.org/I132118926"],"apc_list":null,"apc_paid":null,"fwci":3.108,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.93018426,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"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":0.9998999834060669,"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":0.9998999834060669,"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":0.9991000294685364,"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":0.9987000226974487,"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/computer-science","display_name":"Computer science","score":0.8029329180717468},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6767699122428894},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6559039950370789},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5775109529495239},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5435209274291992},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5401929616928101},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5369290113449097},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5242348313331604},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3909178674221039}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8029329180717468},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6767699122428894},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6559039950370789},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5775109529495239},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5435209274291992},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5401929616928101},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5369290113449097},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5242348313331604},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3909178674221039},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/cnsm.2017.8255998","is_oa":false,"landing_page_url":"https://doi.org/10.23919/cnsm.2017.8255998","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Network and Service Management (CNSM)","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":46,"referenced_works":["https://openalex.org/W26074071","https://openalex.org/W28412257","https://openalex.org/W1598064945","https://openalex.org/W1965018420","https://openalex.org/W1966764836","https://openalex.org/W1966809779","https://openalex.org/W1976821017","https://openalex.org/W1988790447","https://openalex.org/W1990396441","https://openalex.org/W2007759057","https://openalex.org/W2022974299","https://openalex.org/W2024621423","https://openalex.org/W2040333627","https://openalex.org/W2046934276","https://openalex.org/W2060161902","https://openalex.org/W2074568547","https://openalex.org/W2078799390","https://openalex.org/W2088134864","https://openalex.org/W2096118443","https://openalex.org/W2102150307","https://openalex.org/W2107427360","https://openalex.org/W2112076978","https://openalex.org/W2131166445","https://openalex.org/W2134113644","https://openalex.org/W2144261930","https://openalex.org/W2149576945","https://openalex.org/W2165723722","https://openalex.org/W2173213060","https://openalex.org/W2189465200","https://openalex.org/W2278186031","https://openalex.org/W2342408547","https://openalex.org/W2461897637","https://openalex.org/W2538727084","https://openalex.org/W2622882150","https://openalex.org/W2727698908","https://openalex.org/W2741396778","https://openalex.org/W2750788711","https://openalex.org/W2759212564","https://openalex.org/W2912934387","https://openalex.org/W3172197133","https://openalex.org/W4212883601","https://openalex.org/W4233056867","https://openalex.org/W6601098373","https://openalex.org/W6676769703","https://openalex.org/W6687322159","https://openalex.org/W6743372307"],"related_works":["https://openalex.org/W3014300295","https://openalex.org/W4308112567","https://openalex.org/W2810053714","https://openalex.org/W2791691546","https://openalex.org/W3136979370","https://openalex.org/W2950066684","https://openalex.org/W4298388782","https://openalex.org/W4310989423","https://openalex.org/W3200098538","https://openalex.org/W3158264953"],"abstract_inverted_index":{"The":[0,129,152,271],"application":[1,104],"of":[2,9,80,83,105,126,135,147,195,204,230,254,259,281,288],"machine":[3],"learning":[4,107,269],"models":[5,52,108,258],"to":[6,31,68,76,98,109,140,172,186],"the":[7,17,73,78,103,124,136,145,148,160,177,199,217,246,255,260,276,286],"analysis":[8,79,125,287],"network":[10,111,127,212,225,231,289],"measurement":[11,112,213,226],"problems":[12,34,60,175],"has":[13],"largely":[14],"increased":[15],"in":[16,35,58,87,220],"last":[18],"decade;":[19],"however,":[20],"there":[21],"is":[22,66,72,132],"still":[23],"no":[24],"clear":[25],"best-practice":[26,283],"or":[27,164],"silver":[28],"bullet":[29],"approach":[30,139,171,185],"address":[32,77],"these":[33],"a":[36,55,95,118,133,168,193,206,279,282],"general":[37],"context,":[38],"and":[39,42,143,223,233,237,239,262,266],"only":[40],"adhoc":[41],"tailored":[43],"approaches":[44],"have":[45,53],"been":[46],"evaluated":[47],"so":[48],"far.":[49],"While":[50],"deep-learning":[51],"provided":[54],"major":[56],"breakthrough":[57],"highly-dimensional":[59,84],"such":[61],"as":[62,157,159],"image":[63],"processing,":[64],"it":[65,182],"difficult":[67],"say":[69],"today":[70],"which":[71],"best":[74,161],"model":[75,122,131,201,248,274],"large":[81],"volumes":[82],"data":[85,208],"collected":[86],"operational":[88],"networks.":[89],"In":[90,180],"this":[91,100],"paper":[92],"we":[93],"present":[94],"potential":[96],"solution":[97,219],"fill":[99],"gap,":[101],"exploring":[102],"ensemble":[106,141,268],"multiple":[110,174],"problems.":[113],"We":[114,197,215],"introduce":[115],"GML":[116,130,200,247,272],"Learning,":[117],"generic":[119],"Machine":[120],"Learning":[121,273],"for":[123,211,278,285],"measurements.":[128,290],"generalization":[134,280],"well-known":[137],"stacking":[138],"learning,":[142],"follows":[144],"concepts":[146],"Super":[149,153],"Learner":[150,154],"model.":[151],"performs":[155],"asymptotically":[156],"well":[158],"input":[162],"base":[163],"weak":[165],"learners,":[166],"providing":[167],"very":[169],"powerful":[170],"tackle":[173],"with":[176],"same":[178],"technique.":[179],"addition,":[181],"defines":[183],"an":[184],"minimize":[187],"over-fitting":[188],"likelihood":[189],"during":[190],"training,":[191],"using":[192],"variant":[194],"cross-validation.":[196],"deploy":[198],"on":[202],"top":[203],"Big-DAMA,":[205],"big":[207],"analytics":[209],"framework":[210],"applications.":[214],"test":[216],"proposed":[218],"five":[221],"different":[222],"assorted":[224],"problems,":[227],"including":[228],"detection":[229],"attacks":[232],"anomalies,":[234],"QoE":[235],"modeling":[236],"prediction,":[238],"Internet-paths":[240],"dynamics":[241],"tracking.":[242],"Results":[243],"confirm":[244],"that":[245],"provides":[249],"better":[250],"results":[251],"than":[252],"any":[253],"single":[256],"baseline":[257],"stack,":[261],"outperforms":[263],"traditional":[264],"bagging":[265],"boosting":[267],"approaches.":[270],"opens":[275],"door":[277],"technique":[284]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}