{"id":"https://openalex.org/W3005800115","doi":"https://doi.org/10.1109/lcn44214.2019.8990758","title":"RL-Sketch: Scaling Reinforcement Learning for Adaptive and Automate Anomaly Detection in Network Data Streams","display_name":"RL-Sketch: Scaling Reinforcement Learning for Adaptive and Automate Anomaly Detection in Network Data Streams","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3005800115","doi":"https://doi.org/10.1109/lcn44214.2019.8990758","mag":"3005800115"},"language":"en","primary_location":{"id":"doi:10.1109/lcn44214.2019.8990758","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lcn44214.2019.8990758","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 44th Conference on Local Computer Networks (LCN)","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/A5011451620","display_name":"Zhengyan Zhou","orcid":"https://orcid.org/0000-0003-0536-3196"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengyan Zhou","raw_affiliation_strings":["Fuzhou University,College of Mathematics and Computer Science","College of Mathematics and Computer Science, Fuzhou University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuzhou University,College of Mathematics and Computer Science","institution_ids":["https://openalex.org/I80947539"]},{"raw_affiliation_string":"College of Mathematics and Computer Science, Fuzhou University","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004328556","display_name":"Dong Zhang","orcid":"https://orcid.org/0000-0001-7297-5083"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Zhang","raw_affiliation_strings":["Fuzhou University,College of Mathematics and Computer Science","College of Mathematics and Computer Science, Fuzhou University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuzhou University,College of Mathematics and Computer Science","institution_ids":["https://openalex.org/I80947539"]},{"raw_affiliation_string":"College of Mathematics and Computer Science, Fuzhou University","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102002672","display_name":"Xiaoyan Hong","orcid":"https://orcid.org/0000-0002-0745-5038"},"institutions":[{"id":"https://openalex.org/I17301866","display_name":"University of Alabama","ror":"https://ror.org/03xrrjk67","country_code":"US","type":"education","lineage":["https://openalex.org/I17301866"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoyan Hong","raw_affiliation_strings":["University of Alabama,Department of Computer Science","Department of Computer Science, University of Alabama"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Alabama,Department of Computer Science","institution_ids":["https://openalex.org/I17301866"]},{"raw_affiliation_string":"Department of Computer Science, University of Alabama","institution_ids":["https://openalex.org/I17301866"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5579,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.72316209,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"340","last_page":"347"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":1.0,"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":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998000264167786,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9998000264167786,"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":0.8253496885299683},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7547743320465088},{"id":"https://openalex.org/keywords/sketch","display_name":"Sketch","score":0.7014313340187073},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.635576605796814},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6160889863967896},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5991759896278381},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40944424271583557},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3552919924259186},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3465521037578583},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.17931166291236877}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8253496885299683},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7547743320465088},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.7014313340187073},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.635576605796814},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6160889863967896},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5991759896278381},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40944424271583557},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3552919924259186},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3465521037578583},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.17931166291236877},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lcn44214.2019.8990758","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lcn44214.2019.8990758","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 44th Conference on Local Computer Networks (LCN)","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":41,"referenced_works":["https://openalex.org/W32403112","https://openalex.org/W1459214527","https://openalex.org/W1493892051","https://openalex.org/W1515851193","https://openalex.org/W1595483645","https://openalex.org/W1858168446","https://openalex.org/W1865797552","https://openalex.org/W1977951613","https://openalex.org/W1988115241","https://openalex.org/W1999952442","https://openalex.org/W2037536649","https://openalex.org/W2060324268","https://openalex.org/W2072133873","https://openalex.org/W2080234606","https://openalex.org/W2102481563","https://openalex.org/W2139418546","https://openalex.org/W2145339207","https://openalex.org/W2155968351","https://openalex.org/W2167450247","https://openalex.org/W2305788608","https://openalex.org/W2334782222","https://openalex.org/W2487095677","https://openalex.org/W2562649904","https://openalex.org/W2572562038","https://openalex.org/W2605823630","https://openalex.org/W2737988660","https://openalex.org/W2743723076","https://openalex.org/W2746553466","https://openalex.org/W2834288129","https://openalex.org/W2864823780","https://openalex.org/W2885706784","https://openalex.org/W2887192039","https://openalex.org/W2919413299","https://openalex.org/W2969998124","https://openalex.org/W4214717370","https://openalex.org/W4249843299","https://openalex.org/W6635465357","https://openalex.org/W6639080731","https://openalex.org/W6681861716","https://openalex.org/W6698379459","https://openalex.org/W6753705544"],"related_works":["https://openalex.org/W3074294383","https://openalex.org/W4294811414","https://openalex.org/W2364921833","https://openalex.org/W4285805438","https://openalex.org/W2302028273","https://openalex.org/W1525643724","https://openalex.org/W4206669594","https://openalex.org/W2037536649","https://openalex.org/W4319083788","https://openalex.org/W4226248369"],"abstract_inverted_index":{"When":[0],"network":[1,45,66,112],"is":[2,23,87],"undergoing":[3],"problems,":[4],"such":[5,77],"as":[6],"DDoS":[7],"attack,":[8],"component":[9],"failures,":[10],"etc.,":[11],"the":[12,35,52,85,109],"detection":[13,37],"of":[14,38,111],"heavy":[15,18,21,39,100,105],"flows":[16,40,106],"(e.g.":[17],"hitters":[19],"and":[20,51,119],"changers)":[22],"much":[24],"more":[25],"critical.":[26],"However,":[27],"it":[28],"has":[29],"been":[30],"increasing":[31],"challenging":[32],"to":[33,73,81,114,138],"ensure":[34],"accurate":[36],"while":[41,142],"dealing":[42],"with":[43,121,136],"massive":[44],"traffic":[46,49],"volume,":[47],"diversified":[48],"distribution":[50],"stringent":[53],"memory":[54,72,86],"requirement.":[55],"Although":[56],"recent":[57],"research":[58],"efforts":[59],"like":[60],"LD-Sketch":[61],"are":[62],"scalable":[63],"for":[64],"diverse":[65],"traffic,":[67,113],"they":[68,79],"depend":[69],"on":[70,108],"excessive":[71],"maintain":[74],"high":[75,117,144],"accuracy,":[76],"that":[78,127],"fail":[80],"work":[82],"well":[83],"when":[84],"limited.":[88],"We":[89],"propose":[90],"RL-Sketch,":[91],"a":[92],"adaptive":[93],"sketch":[94],"using":[95],"reinforcement":[96],"learning":[97],"in":[98,146],"detecting":[99],"flows.":[101],"It":[102],"predicts":[103],"potential":[104],"based":[107],"statistics":[110],"achieve":[115],"both":[116],"accuracy":[118,131,140],"scalability":[120],"minor":[122],"memory.":[123],"Trace-driven":[124],"evaluation":[125],"shows":[126],"RL-Sketch":[128],"achieves":[129],"higher":[130],"than":[132],"state-of-the-art":[133],"sketch-based":[134],"technologies":[135],"up":[137],"17.79\u00d7":[139],"gain,":[141],"maintaining":[143],"robustness":[145],"extreme":[147],"conditions.":[148]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}