{"id":"https://openalex.org/W3160115074","doi":"https://doi.org/10.1145/3483207.3483232","title":"ADASYN\u2212Random Forest Based Intrusion Detection Model","display_name":"ADASYN\u2212Random Forest Based Intrusion Detection Model","publication_year":2021,"publication_date":"2021-08-18","ids":{"openalex":"https://openalex.org/W3160115074","doi":"https://doi.org/10.1145/3483207.3483232","mag":"3160115074"},"language":"en","primary_location":{"id":"doi:10.1145/3483207.3483232","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3483207.3483232","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 4th International Conference on Signal Processing and Machine Learning","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2105.04301","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103192430","display_name":"Zhewei Chen","orcid":"https://orcid.org/0009-0004-6778-1988"},"institutions":[{"id":"https://openalex.org/I135237710","display_name":"Zhejiang Normal University","ror":"https://ror.org/01vevwk45","country_code":"CN","type":"education","lineage":["https://openalex.org/I135237710"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhewei Chen","raw_affiliation_strings":["Zhejiang Normal University, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang Normal University, China","institution_ids":["https://openalex.org/I135237710"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078284441","display_name":"Linyue Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I135237710","display_name":"Zhejiang Normal University","ror":"https://ror.org/01vevwk45","country_code":"CN","type":"education","lineage":["https://openalex.org/I135237710"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linyue Zhou","raw_affiliation_strings":["Zhejiang Normal University, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang Normal University, China","institution_ids":["https://openalex.org/I135237710"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007178447","display_name":"Wenwen Yu","orcid":"https://orcid.org/0000-0003-4666-3646"},"institutions":[{"id":"https://openalex.org/I159389169","display_name":"Ningbo University of Technology","ror":"https://ror.org/037dym702","country_code":"CN","type":"education","lineage":["https://openalex.org/I159389169"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenwen Yu","raw_affiliation_strings":["NingboTech University, China"],"affiliations":[{"raw_affiliation_string":"NingboTech University, China","institution_ids":["https://openalex.org/I159389169"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103192430"],"corresponding_institution_ids":["https://openalex.org/I135237710"],"apc_list":null,"apc_paid":null,"fwci":0.3185,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.59293032,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"152","last_page":"159"},"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":0.9979000091552734,"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":0.991100013256073,"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/intrusion-detection-system","display_name":"Intrusion detection system","score":0.774390459060669},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7707811594009399},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.7666324973106384},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5784765481948853},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5485497713088989},{"id":"https://openalex.org/keywords/intrusion","display_name":"Intrusion","score":0.5309996604919434},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5167067050933838},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5095327496528625},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.43367132544517517},{"id":"https://openalex.org/keywords/network-security","display_name":"Network security","score":0.4246307611465454},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.422818124294281},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33855682611465454},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.0911998450756073},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08800697326660156}],"concepts":[{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.774390459060669},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7707811594009399},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7666324973106384},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5784765481948853},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5485497713088989},{"id":"https://openalex.org/C158251709","wikidata":"https://www.wikidata.org/wiki/Q354025","display_name":"Intrusion","level":2,"score":0.5309996604919434},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5167067050933838},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5095327496528625},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.43367132544517517},{"id":"https://openalex.org/C182590292","wikidata":"https://www.wikidata.org/wiki/Q989632","display_name":"Network security","level":2,"score":0.4246307611465454},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.422818124294281},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33855682611465454},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0911998450756073},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08800697326660156},{"id":"https://openalex.org/C17409809","wikidata":"https://www.wikidata.org/wiki/Q161764","display_name":"Geochemistry","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},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3483207.3483232","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3483207.3483232","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 4th International Conference on Signal Processing and Machine Learning","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2105.04301","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2105.04301","pdf_url":"https://arxiv.org/pdf/2105.04301","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2105.04301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2105.04301","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"},{"id":"mag:3160115074","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2105.04301","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2105.04301","pdf_url":"https://arxiv.org/pdf/2105.04301","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2092540443","https://openalex.org/W2311127170","https://openalex.org/W2889885794","https://openalex.org/W2902805725","https://openalex.org/W2908373224","https://openalex.org/W3089011873","https://openalex.org/W7024208160"],"related_works":["https://openalex.org/W3209772198","https://openalex.org/W3097529860","https://openalex.org/W2512144135","https://openalex.org/W2188001659","https://openalex.org/W2903369217","https://openalex.org/W836876797","https://openalex.org/W3156726139","https://openalex.org/W2983194513","https://openalex.org/W3138011120","https://openalex.org/W2995758142","https://openalex.org/W1928993760","https://openalex.org/W2962910311","https://openalex.org/W2783047817","https://openalex.org/W2944146427","https://openalex.org/W3156022694","https://openalex.org/W3197913358","https://openalex.org/W2751702912","https://openalex.org/W2593203374","https://openalex.org/W1512042544","https://openalex.org/W2047408376"],"abstract_inverted_index":{"Intrusion":[0,87],"detection":[1,32,80,88,134],"has":[2,156],"been":[3],"a":[4],"key":[5],"topic":[6],"in":[7,36,67],"the":[8,14,20,27,83,105,108,126,142],"field":[9],"of":[10,22,30,43,86,110,145],"cyber":[11],"security,":[12],"and":[13,24,47,54,115,138,161],"common":[15],"network":[16,51,146],"threats":[17],"nowadays":[18],"have":[19],"characteristics":[21],"varieties":[23],"variation.":[25],"Considering":[26],"serious":[28],"imbalance":[29],"intrusion":[31,79,133],"datasets":[33,64],"will":[34],"result":[35],"low":[37],"classification":[38,143],"performance":[39],"on":[40,89,104],"attack":[41,147],"behaviors":[42],"small":[44],"sample":[45],"size":[46],"difficulty":[48],"to":[49,62,77,132],"detect":[50],"attacks":[52],"accurately":[53],"efficiently,":[55],"using":[56],"Adaptive":[57],"Synthetic":[58],"Sampling":[59],"(ADASYN)":[60],"method":[61,128],"balance":[63],"was":[65,75],"proposed":[66,127],"this":[68],"paper.":[69],"In":[70],"addition,":[71],"Random":[72,99],"Forest":[73,100],"algorithm":[74],"used":[76],"train":[78],"classifiers.":[81],"Through":[82],"comparative":[84],"experiment":[85],"CICIDS":[90],"2017":[91],"dataset,":[92],"it":[93,155],"is":[94,120],"found":[95],"that":[96,125],"ADASYN":[97,119],"with":[98,135,150],"performs":[101],"better.":[102],"Based":[103],"experimental":[106],"results,":[107],"improvement":[109],"precision,":[111],"recall,":[112],"F1":[113],"scores":[114],"AUC":[116],"values":[117],"after":[118],"then":[121],"analyzed.":[122],"Experiments":[123],"show":[124],"can":[129,139],"be":[130],"applied":[131],"large":[136],"data,":[137],"effectively":[140],"improve":[141],"accuracy":[144],"behaviors.":[148],"Compared":[149],"traditional":[151],"machine":[152],"learning":[153],"models,":[154],"better":[157],"performance,":[158],"generalization":[159],"ability":[160],"robustness.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
