{"id":"https://openalex.org/W4414458674","doi":"https://doi.org/10.1109/jiot.2025.3613994","title":"Causal Gray Wolf Optimization: A Novel Approach to Robust Network Anomaly Detection Amidst Data Redundancy","display_name":"Causal Gray Wolf Optimization: A Novel Approach to Robust Network Anomaly Detection Amidst Data Redundancy","publication_year":2025,"publication_date":"2025-09-24","ids":{"openalex":"https://openalex.org/W4414458674","doi":"https://doi.org/10.1109/jiot.2025.3613994"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2025.3613994","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2025.3613994","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-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/A5028312915","display_name":"Zengri Zeng","orcid":"https://orcid.org/0000-0002-5329-0713"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]},{"id":"https://openalex.org/I257545875","display_name":"Hunan University of Humanities, Science and Technology","ror":"https://ror.org/00s7jmd98","country_code":"CN","type":"education","lineage":["https://openalex.org/I257545875"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zengri Zeng","raw_affiliation_strings":["School of Information, Science and Technology, Hunan University of Humanities, Loudi, Hunan, China","College of Computers, National University of Defense Technology, Changsha, Hunan, China"],"raw_orcid":"https://orcid.org/0000-0002-5329-0713","affiliations":[{"raw_affiliation_string":"School of Information, Science and Technology, Hunan University of Humanities, Loudi, Hunan, China","institution_ids":["https://openalex.org/I257545875"]},{"raw_affiliation_string":"College of Computers, National University of Defense Technology, Changsha, Hunan, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086816845","display_name":"Xiaoheng Deng","orcid":"https://orcid.org/0000-0003-2740-8025"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoheng Deng","raw_affiliation_strings":["College of Electronic Information, Central South University, Changsha, China","College of Electronic Information, professor, Central South University, Changsha, Hunan, China"],"raw_orcid":"https://orcid.org/0000-0003-2740-8025","affiliations":[{"raw_affiliation_string":"College of Electronic Information, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]},{"raw_affiliation_string":"College of Electronic Information, professor, Central South University, Changsha, Hunan, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055202882","display_name":"Baokang Zhao","orcid":"https://orcid.org/0000-0001-9200-9018"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]},{"id":"https://openalex.org/I257545875","display_name":"Hunan University of Humanities, Science and Technology","ror":"https://ror.org/00s7jmd98","country_code":"CN","type":"education","lineage":["https://openalex.org/I257545875"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baokang Zhao","raw_affiliation_strings":["School of Information, Science and Technology, Hunan University of Humanities, Loudi, Hunan, China","College of Computers, associate professor, National University of Defense Technology, Changsha, Hunan, China"],"raw_orcid":"https://orcid.org/0000-0001-9200-9018","affiliations":[{"raw_affiliation_string":"School of Information, Science and Technology, Hunan University of Humanities, Loudi, Hunan, China","institution_ids":["https://openalex.org/I257545875"]},{"raw_affiliation_string":"College of Computers, associate professor, National University of Defense Technology, Changsha, Hunan, China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5028312915"],"corresponding_institution_ids":["https://openalex.org/I170215575","https://openalex.org/I257545875"],"apc_list":null,"apc_paid":null,"fwci":2.1733,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.90511759,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"12","issue":"24","first_page":"52820","last_page":"52836"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9535999894142151,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9535999894142151,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9182999730110168,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.722599983215332},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5174000263214111},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.49129998683929443},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4659999907016754},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.44020000100135803},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.43560001254081726},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4138999879360199},{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.4075999855995178},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.3698999881744385},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.3589000105857849}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7846999764442444},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.722599983215332},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.590399980545044},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5331000089645386},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5174000263214111},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.49129998683929443},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4659999907016754},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46560001373291016},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.44020000100135803},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.43560001254081726},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4138999879360199},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.4075999855995178},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3698999881744385},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.3589000105857849},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.35670000314712524},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.353300005197525},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.34630000591278076},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.321399986743927},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.2635999917984009},{"id":"https://openalex.org/C2781215313","wikidata":"https://www.wikidata.org/wiki/Q3493345","display_name":"SPARK (programming language)","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2025.3613994","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2025.3613994","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8859756302","display_name":null,"funder_award_id":"no.61972412, 62172441, 62172449, 62373372","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W2165707182","https://openalex.org/W2741791470","https://openalex.org/W2807992610","https://openalex.org/W2964254462","https://openalex.org/W2964718997","https://openalex.org/W2979998718","https://openalex.org/W2984419450","https://openalex.org/W3026161804","https://openalex.org/W3033675321","https://openalex.org/W3035254752","https://openalex.org/W3035439169","https://openalex.org/W3035536174","https://openalex.org/W3048313003","https://openalex.org/W3080286822","https://openalex.org/W3092565864","https://openalex.org/W3093410479","https://openalex.org/W3105087971","https://openalex.org/W3133726397","https://openalex.org/W4200572601","https://openalex.org/W4224216588","https://openalex.org/W4286579686","https://openalex.org/W4292506131","https://openalex.org/W4296117805","https://openalex.org/W4312673899","https://openalex.org/W4327740859","https://openalex.org/W4387328039","https://openalex.org/W4388430530","https://openalex.org/W4389328382","https://openalex.org/W4390542272","https://openalex.org/W4390970197","https://openalex.org/W4391326527","https://openalex.org/W4392040984","https://openalex.org/W4392359842","https://openalex.org/W4395034421","https://openalex.org/W4396682736","https://openalex.org/W4399555917","https://openalex.org/W4404654652","https://openalex.org/W4405270529","https://openalex.org/W4406753679","https://openalex.org/W4409760173"],"related_works":[],"abstract_inverted_index":{"The":[0,144],"rapid":[1],"proliferation":[2],"of":[3,12,36,172],"IoT":[4],"technologies":[5],"intensifies":[6],"network":[7,184],"anomaly":[8,85],"detection":[9,86],"challenges":[10],"because":[11],"high-dimensional":[13],"and":[14,81,83,114,127,162,178],"redundant":[15,102],"data.":[16],"To":[17],"address":[18],"this":[19],"issue,":[20],"we":[21],"propose":[22],"causal":[23,42,46,60,64,95],"gray":[24,37],"wolf":[25,38],"optimization":[26,39,74],"(CGWO),":[27],"a":[28,53,76],"framework":[29,145],"that":[30],"integrates":[31],"the":[32,170],"global":[33],"search":[34],"capabilities":[35],"(GWO)":[40],"with":[41,59,87,110,125,135],"inference":[43],"to":[44],"distinguish":[45],"features":[47,103],"from":[48],"spurious":[49],"correlations.":[50],"CGWO":[51,100,116,173],"employs":[52],"four-stage":[54],"process:":[55],"(1)":[56],"data":[57],"preprocessing":[58],"weight":[61],"transformation,":[62],"(2)":[63],"analysis":[65],"via":[66],"potential":[67],"outcome":[68],"models":[69],"(POMs),":[70],"(3)":[71],"feature":[72,147],"subset":[73],"using":[75],"fitness":[77],"function":[78],"balancing":[79,175],"accuracy":[80,119,129],"dimensionality,":[82],"(4)":[84],"an":[88],"enhanced":[89],"convolutional":[90],"autoencoder":[91],"(ECAE).":[92],"By":[93],"prioritizing":[94],"relationships":[96],"over":[97],"statistical":[98],"correlations,":[99],"minimizes":[101],"while":[104],"increasing":[105],"model":[106,180],"interpretability.":[107],"When":[108],"evaluated":[109],"benchmark":[111],"datasets":[112],"(NSL-KDD":[113],"UNSW-NB15),":[115],"achieves":[117],"99.8%":[118],"(14":[120],"features)":[121,131],"for":[122,132,164,182],"5-class":[123],"classification":[124,134],"NSL-KDD":[126],"94.2%":[128],"(10":[130],"10-class":[133],"UNSW-NB15,":[136],"outperforming":[137],"conventional":[138],"methods":[139],"by":[140,149],"more":[141],"than":[142],"6%.":[143],"reduces":[146],"dimensions":[148],"65\u201380%":[150],"without":[151],"performance":[152],"loss,":[153],"demonstrating":[154],"robustness,":[155],"computational":[156],"efficiency":[157],"(120\u2013150":[158],"s":[159],"per":[160],"dataset),":[161],"scalability":[163],"edge":[165],"computing.":[166],"These":[167],"results":[168],"validate":[169],"effectiveness":[171],"in":[174],"dimensionality":[176],"reduction":[177],"interpretable":[179],"design":[181],"complex":[183],"environments.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-11T23:09:37.256380","created_date":"2025-10-10T00:00:00"}