{"id":"https://openalex.org/W2963360831","doi":"https://doi.org/10.1109/pimrc.2017.8292316","title":"Deep learning for malicious flow detection","display_name":"Deep learning for malicious flow detection","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2963360831","doi":"https://doi.org/10.1109/pimrc.2017.8292316","mag":"2963360831"},"language":"en","primary_location":{"id":"doi:10.1109/pimrc.2017.8292316","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc.2017.8292316","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)","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/A5082609714","display_name":"Yun-Chun Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Yun-Chun Chen","raw_affiliation_strings":["Department of Electrical Engineering, National Taiwan University"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Taiwan University","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015746504","display_name":"Yu-Jhe Li","orcid":null},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yu-Jhe Li","raw_affiliation_strings":["Graduate Institute of Communication Engineering, National Taiwan University"],"affiliations":[{"raw_affiliation_string":"Graduate Institute of Communication Engineering, National Taiwan University","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033509483","display_name":"Aragorn Tseng","orcid":null},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Aragorn Tseng","raw_affiliation_strings":["Graduate Institute of Communication Engineering, National Taiwan University"],"affiliations":[{"raw_affiliation_string":"Graduate Institute of Communication Engineering, National Taiwan University","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039482466","display_name":"Tsung-Nan Lin","orcid":"https://orcid.org/0000-0001-5659-1194"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]},{"id":"https://openalex.org/I3141939062","display_name":"Institute for Information Industry","ror":"https://ror.org/01d8kr740","country_code":"TW","type":"nonprofit","lineage":["https://openalex.org/I3141939062"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Tsungnan Lin","raw_affiliation_strings":["Cybersecurity Technology Institute, Institute for Information Industry","Graduate Institute of Communication Engineering, National Taiwan University"],"affiliations":[{"raw_affiliation_string":"Cybersecurity Technology Institute, Institute for Information Industry","institution_ids":["https://openalex.org/I3141939062"]},{"raw_affiliation_string":"Graduate Institute of Communication Engineering, National Taiwan University","institution_ids":["https://openalex.org/I16733864"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5082609714"],"corresponding_institution_ids":["https://openalex.org/I16733864"],"apc_list":null,"apc_paid":null,"fwci":4.7656,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.95744847,"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":"7"},"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":0.9998999834060669,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9994999766349792,"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/computer-science","display_name":"Computer science","score":0.8251214027404785},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.768641471862793},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7063572406768799},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6991539001464844},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.617917537689209},{"id":"https://openalex.org/keywords/malware","display_name":"Malware","score":0.5880477428436279},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5110594034194946},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5088256001472473},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.5040208697319031},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4885045289993286},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.45053809881210327},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.44234994053840637},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.42353835701942444},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35532695055007935},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.17837917804718018},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08235698938369751}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8251214027404785},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.768641471862793},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7063572406768799},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6991539001464844},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.617917537689209},{"id":"https://openalex.org/C541664917","wikidata":"https://www.wikidata.org/wiki/Q14001","display_name":"Malware","level":2,"score":0.5880477428436279},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5110594034194946},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5088256001472473},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.5040208697319031},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4885045289993286},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.45053809881210327},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.44234994053840637},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.42353835701942444},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35532695055007935},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.17837917804718018},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08235698938369751},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/pimrc.2017.8292316","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc.2017.8292316","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W179875071","https://openalex.org/W1524496078","https://openalex.org/W1966180580","https://openalex.org/W2020909399","https://openalex.org/W2072947655","https://openalex.org/W2073089243","https://openalex.org/W2104167780","https://openalex.org/W2163605009","https://openalex.org/W2166844173","https://openalex.org/W2317592095","https://openalex.org/W2340189285","https://openalex.org/W2566050683","https://openalex.org/W2566926062","https://openalex.org/W2743556905","https://openalex.org/W2952812854","https://openalex.org/W2963065250","https://openalex.org/W3007346474","https://openalex.org/W6607333740","https://openalex.org/W6684191040","https://openalex.org/W7027738607"],"related_works":["https://openalex.org/W4388103534","https://openalex.org/W4312531262","https://openalex.org/W4318142952","https://openalex.org/W2072070946","https://openalex.org/W2149903055","https://openalex.org/W2786391746","https://openalex.org/W3132346564","https://openalex.org/W2991483587","https://openalex.org/W2914559142","https://openalex.org/W4226059458"],"abstract_inverted_index":{"Cyber":[0],"security":[1],"has":[2],"grown":[3],"up":[4],"to":[5,14,52,79,155],"be":[6],"a":[7,19,53,59,69,90,101,113,165,177],"hot":[8],"issue":[9,44],"in":[10,68,100,189],"recent":[11],"years.":[12],"How":[13],"identify":[15],"potential":[16],"malware":[17],"becomes":[18],"challenging":[20],"task.":[21],"To":[22,104],"tackle":[23],"this":[24,62,86],"challenge,":[25],"we":[26,88,111],"adopt":[27],"deep":[28,187],"learning":[29,179,188],"approaches":[30],"and":[31,147,176],"perform":[32],"flow":[33,167],"detection":[34,175],"on":[35,132],"real":[36,39],"data.":[37],"However,":[38],"data":[40,47,99,135],"often":[41],"encounters":[42],"an":[43,133],"of":[45,123,159,173,186],"imbalanced":[46,134,160],"distribution":[48],"which":[49,96,119,169,181],"will":[50,64],"lead":[51],"gradient":[54],"dilution":[55],"issue.":[56],"When":[57],"training":[58],"neural":[60],"network,":[61],"problem":[63],"not":[65],"only":[66],"result":[67,138],"bias":[70],"toward":[71],"the":[72,77,82,98,108,121,124,144,150,157,171,183],"majority":[73],"class":[74],"but":[75],"show":[76],"inability":[78],"learn":[80,106],"from":[81,107],"minority":[83,109],"classes.":[84,127],"In":[85],"paper,":[87],"propose":[89,112],"Tree-Shaped":[91],"Deep":[92],"Neural":[93],"Network":[94],"(TSDNN)":[95],"classifies":[97],"layer-wise":[102],"manner.":[103],"better":[105],"classes,":[110],"Quantity":[114],"Dependent":[115],"Backpropagation":[116],"(QDBP)":[117],"algorithm":[118],"incorporates":[120],"knowledge":[122],"disparity":[125],"between":[126],"We":[128,162],"evaluate":[129],"our":[130,141],"method":[131,152],"set.":[136],"Experimental":[137],"demonstrates":[139],"that":[140,149],"approach":[142],"outperforms":[143],"state-of-the-art":[145],"methods":[146],"justifies":[148,182],"proposed":[151],"is":[153],"able":[154],"overcome":[156],"difficulty":[158],"learning.":[161],"also":[163],"conduct":[164],"partial":[166],"experiment":[168,180],"shows":[170],"feasibility":[172],"realtime":[174],"zero-shot":[178],"generalization":[184],"capability":[185],"cyber":[190],"security.":[191]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":12},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}