{"id":"https://openalex.org/W2903199472","doi":"https://doi.org/10.1109/nca.2018.8548325","title":"The Effect on Network Flows-Based Features and Training Set Size on Malware Detection","display_name":"The Effect on Network Flows-Based Features and Training Set Size on Malware Detection","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2903199472","doi":"https://doi.org/10.1109/nca.2018.8548325","mag":"2903199472"},"language":"en","primary_location":{"id":"doi:10.1109/nca.2018.8548325","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nca.2018.8548325","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)","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/A5008949253","display_name":"Jarilyn M. Hern\u00e1ndez Jim\u00e9nez","orcid":null},"institutions":[{"id":"https://openalex.org/I12097938","display_name":"West Virginia University","ror":"https://ror.org/011vxgd24","country_code":"US","type":"education","lineage":["https://openalex.org/I12097938"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jarilyn M. Hernandez Jimenez","raw_affiliation_strings":["Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV","institution_ids":["https://openalex.org/I12097938"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008905681","display_name":"Katerina Go\u0161eva-Popstojanova","orcid":"https://orcid.org/0000-0003-4683-672X"},"institutions":[{"id":"https://openalex.org/I12097938","display_name":"West Virginia University","ror":"https://ror.org/011vxgd24","country_code":"US","type":"education","lineage":["https://openalex.org/I12097938"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Katerina Goseva-Popstojanova","raw_affiliation_strings":["Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV","institution_ids":["https://openalex.org/I12097938"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I12097938"],"apc_list":null,"apc_paid":null,"fwci":1.2628,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.82031551,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9998000264167786,"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":0.9987999796867371,"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/malware","display_name":"Malware","score":0.8548761606216431},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7472820281982422},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6892725229263306},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5395196676254272},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5376222729682922},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5057556629180908},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.395535409450531},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3328050971031189},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.1896894872188568}],"concepts":[{"id":"https://openalex.org/C541664917","wikidata":"https://www.wikidata.org/wiki/Q14001","display_name":"Malware","level":2,"score":0.8548761606216431},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7472820281982422},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6892725229263306},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5395196676254272},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5376222729682922},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5057556629180908},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.395535409450531},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3328050971031189},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.1896894872188568},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/nca.2018.8548325","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nca.2018.8548325","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)","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"},{"id":"https://openalex.org/F4320338287","display_name":"Oak Ridge National Laboratory","ror":"https://ror.org/01qz5mb56"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W40890042","https://openalex.org/W200681053","https://openalex.org/W757277879","https://openalex.org/W1504694836","https://openalex.org/W1528113134","https://openalex.org/W1639956611","https://openalex.org/W1912123407","https://openalex.org/W1985328160","https://openalex.org/W2016305672","https://openalex.org/W2025560520","https://openalex.org/W2034053858","https://openalex.org/W2036575863","https://openalex.org/W2051725773","https://openalex.org/W2057787526","https://openalex.org/W2061486139","https://openalex.org/W2074023167","https://openalex.org/W2079239688","https://openalex.org/W2119954997","https://openalex.org/W2125055259","https://openalex.org/W2133990480","https://openalex.org/W2147986322","https://openalex.org/W2148727298","https://openalex.org/W2171035369","https://openalex.org/W2182464523","https://openalex.org/W2188584537","https://openalex.org/W2331488455","https://openalex.org/W2406037958","https://openalex.org/W2541577949","https://openalex.org/W2544088398","https://openalex.org/W2612737649","https://openalex.org/W2613459913","https://openalex.org/W2768073432","https://openalex.org/W2773948549","https://openalex.org/W2775173651","https://openalex.org/W2784097977","https://openalex.org/W2795067647","https://openalex.org/W2798790152","https://openalex.org/W2911964244","https://openalex.org/W4250463209","https://openalex.org/W6608206699","https://openalex.org/W6631388279","https://openalex.org/W6636587085","https://openalex.org/W6640114639","https://openalex.org/W6668794753","https://openalex.org/W6687341372","https://openalex.org/W6713761039","https://openalex.org/W6729439378","https://openalex.org/W6750036672"],"related_works":["https://openalex.org/W2968586400","https://openalex.org/W4316087074","https://openalex.org/W3083609968","https://openalex.org/W3201070945","https://openalex.org/W4287395004","https://openalex.org/W3121393540","https://openalex.org/W3118798877","https://openalex.org/W3099765033","https://openalex.org/W2792951589","https://openalex.org/W3003942501"],"abstract_inverted_index":{"Although":[0],"network":[1,10,21,41,107],"flows":[2],"have":[3,19],"been":[4],"used":[5,20,74,220,253],"in":[6,187],"areas":[7],"such":[8],"as":[9,148,172,213],"traffic":[11,42],"analysis":[12],"and":[13,43,62,67,119,130,241],"botnet":[14],"detection,":[15],"not":[16,230],"many":[17],"works":[18],"flows-based":[22,108],"features":[23,37,87,109,139,162,176,184],"for":[24,64,89,221,239,243,254],"malware":[25,32,65,115],"detection.":[26,116],"This":[27],"paper":[28],"is":[29],"focused":[30],"on":[31,35,78,197,206],"detection":[33,66],"based":[34,77],"using":[36,150],"extracted":[38],"from":[39],"the":[40,48,69,83,112,122,127,136,145,155,159,169,181,200,209,216,250],"system":[44,182],"logs.":[45],"We":[46],"evaluated":[47],"performance":[49,113,147,171,194,233],"of":[50,86,98,114,157,180,199,208,215,226,249],"four":[51],"supervised":[52],"machine":[53],"learning":[54],"algorithms":[55],"(i.e.,":[56,235],"J48,":[57,135],"Random":[58],"Forest,":[59],"Naive":[60],"Bayes,":[61],"PART)":[63],"identified":[68],"best":[70,123],"learner.":[71],"Furthermore,":[72],"we":[73,93],"feature":[75],"selection":[76],"information":[79,142,165],"gain":[80,143,166],"to":[81,168,204,246],"identify":[82],"smallest":[84],"number":[85],"needed":[88],"classification.":[90],"In":[91,154],"addition,":[92],"experimented":[94],"with":[95,126],"training":[96,196,205,222],"sets":[97],"different":[99],"sizes.":[100],"The":[101,192],"main":[102],"findings":[103],"include:":[104],"(1)":[105],"Adding":[106],"improved":[110],"significantly":[111],"(2)":[117],"J48":[118],"PART":[120],"were":[121,177,185,252],"performing":[124],"learners,":[125],"highest":[128],"F-score":[129,240],"G-score":[131,244],"values.":[132],"(3)":[133],"Using":[134],"top":[137,160],"five":[138],"ranked":[140,163],"by":[141,164],"attained":[144],"same":[146,170],"when":[149,173,195,247],"all":[151,174],"88":[152,175],"features.":[153],"case":[156],"PART,":[158],"fourteen":[161],"led":[167],"used.":[178],"None":[179],"logs-based":[183],"included":[186],"these":[188],"two":[189],"models.":[190],"(4)":[191],"classification":[193],"75%":[198],"data":[201,217,251],"was":[202],"comparable":[203],"90%":[207,248],"data.":[210],"As":[211],"little":[212],"25%":[214],"can":[218],"be":[219],"at":[223],"an":[224],"expense":[225],"somewhat":[227],"higher,":[228],"but":[229],"very":[231],"significant":[232],"degradation":[234],"less":[236],"than":[237],"7%":[238],"6%":[242],"compared":[245],"training).":[255]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
