{"id":"https://openalex.org/W3201510831","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534325","title":"An Intrusion Detection System based on PSO-GWO Hybrid Optimized Support Vector Machine","display_name":"An Intrusion Detection System based on PSO-GWO Hybrid Optimized Support Vector Machine","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3201510831","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534325","mag":"3201510831"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534325","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534325","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","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/A5100329678","display_name":"Kexin Li","orcid":"https://orcid.org/0000-0003-3879-294X"},"institutions":[{"id":"https://openalex.org/I153374732","display_name":"Liaoning Normal University","ror":"https://ror.org/04c3cgg32","country_code":"CN","type":"education","lineage":["https://openalex.org/I153374732"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kexin Li","raw_affiliation_strings":["School of Computer and Information, Technology Liaoning Normal University, Dalian, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information, Technology Liaoning Normal University, Dalian, China","institution_ids":["https://openalex.org/I153374732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100343319","display_name":"Yong Zhang","orcid":"https://orcid.org/0000-0001-9253-4330"},"institutions":[{"id":"https://openalex.org/I153374732","display_name":"Liaoning Normal University","ror":"https://ror.org/04c3cgg32","country_code":"CN","type":"education","lineage":["https://openalex.org/I153374732"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Zhang","raw_affiliation_strings":["School of Computer and Information, Technology Liaoning Normal University, Dalian, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information, Technology Liaoning Normal University, Dalian, China","institution_ids":["https://openalex.org/I153374732"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047862167","display_name":"Shuai Wang","orcid":"https://orcid.org/0000-0002-1381-9678"},"institutions":[{"id":"https://openalex.org/I153374732","display_name":"Liaoning Normal University","ror":"https://ror.org/04c3cgg32","country_code":"CN","type":"education","lineage":["https://openalex.org/I153374732"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Wang","raw_affiliation_strings":["School of Computer and Information, Technology Liaoning Normal University, Dalian, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information, Technology Liaoning Normal University, Dalian, China","institution_ids":["https://openalex.org/I153374732"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100329678"],"corresponding_institution_ids":["https://openalex.org/I153374732"],"apc_list":null,"apc_paid":null,"fwci":1.0697,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.78635495,"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":"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":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/T12326","display_name":"Network Packet Processing and Optimization","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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":0.9916999936103821,"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":0.768840491771698},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7613840103149414},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.7586008906364441},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7346664071083069},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5339898467063904},{"id":"https://openalex.org/keywords/constant-false-alarm-rate","display_name":"Constant false alarm rate","score":0.49557721614837646},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.460788369178772},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.45606672763824463},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4488895833492279},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.44858622550964355},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.385821133852005},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35690969228744507}],"concepts":[{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.768840491771698},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7613840103149414},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.7586008906364441},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7346664071083069},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5339898467063904},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.49557721614837646},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.460788369178772},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.45606672763824463},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4488895833492279},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.44858622550964355},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.385821133852005},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35690969228744507},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9534325","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534325","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2010448560","https://openalex.org/W2061438946","https://openalex.org/W2087327261","https://openalex.org/W2099940443","https://openalex.org/W2152195021","https://openalex.org/W2156909104","https://openalex.org/W2296509296","https://openalex.org/W2397790788","https://openalex.org/W2409097990","https://openalex.org/W2754990055","https://openalex.org/W2807319534","https://openalex.org/W2894006709","https://openalex.org/W2895233886","https://openalex.org/W2987920886","https://openalex.org/W2994866269","https://openalex.org/W3001675796","https://openalex.org/W3002359647","https://openalex.org/W3015355370","https://openalex.org/W3018664167","https://openalex.org/W3026790608","https://openalex.org/W3033213260","https://openalex.org/W3035311645","https://openalex.org/W3042855339","https://openalex.org/W3089155409","https://openalex.org/W3093253286","https://openalex.org/W4240990611","https://openalex.org/W6682642761","https://openalex.org/W6755183427"],"related_works":["https://openalex.org/W2357468538","https://openalex.org/W1577110157","https://openalex.org/W2355007334","https://openalex.org/W2388326001","https://openalex.org/W2998914036","https://openalex.org/W2390009783","https://openalex.org/W2494338568","https://openalex.org/W4254602698","https://openalex.org/W2394461323","https://openalex.org/W2349441905"],"abstract_inverted_index":{"Intrusion":[0],"Detection":[1],"System":[2],"(IDS)":[3],"is":[4,78,131],"an":[5],"important":[6],"tool":[7],"to":[8,56,80,102,107,123],"ensure":[9],"network":[10],"security,":[11],"which":[12],"can":[13],"detect":[14],"and":[15,24,64,73,95,118,128,153,158],"prevent":[16],"malicious":[17],"behavior":[18],"in":[19,48,150],"time.":[20],"However,":[21],"the":[22,30,58,61,82,91,97,103,109,114,125,129],"noise":[23],"redundancy":[25],"of":[26,33,38,60],"data":[27],"often":[28],"reduce":[29,57],"detection":[31,40,151,156],"performance":[32,149],"classifiers.":[34],"The":[35,138],"traditional":[36],"model":[37,67,130,146],"intrusion":[39],"system":[41],"cannot":[42],"effectively":[43],"solve":[44],"this":[45,49,112],"problem.":[46],"Therefore,":[47],"paper,":[50,113],"autoencoders":[51],"(AEs)":[52],"are":[53,121],"firstly":[54],"used":[55,122],"dimension":[59],"original":[62],"data,":[63],"a":[65],"hybrid":[66,144],"combining":[68],"particle":[69],"swarm":[70],"optimization":[71,76,93,145],"(PSO)":[72],"gray":[74],"wolf":[75],"(GWO)":[77],"proposed":[79,126],"optimize":[81],"support":[83],"vector":[84],"machine":[85],"(SVM)":[86],"parameters.":[87],"This":[88],"method":[89],"combines":[90],"two":[92],"algorithms":[94],"selects":[96],"optimal":[98],"parameter":[99],"values":[100],"according":[101],"locally":[104],"enhanced":[105],"particles":[106],"train":[108],"classifier.":[110],"In":[111],"NSL-KDD":[115],"benchmark":[116],"dataset":[117,120],"UNSW-NB15":[119],"evaluate":[124],"model,":[127],"compared":[132],"with":[133],"other":[134],"classification":[135],"methods":[136],"separately.":[137],"experimental":[139],"results":[140],"show":[141],"that":[142],"our":[143],"has":[147],"better":[148],"accuracy":[152],"provides":[154],"good":[155],"rate":[157],"false":[159],"alarm":[160],"rate.":[161]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}