{"id":"https://openalex.org/W1969566588","doi":"https://doi.org/10.1109/cicybs.2009.4925096","title":"An unsupervised method for intrusion detection using spectral clustering","display_name":"An unsupervised method for intrusion detection using spectral clustering","publication_year":2009,"publication_date":"2009-03-01","ids":{"openalex":"https://openalex.org/W1969566588","doi":"https://doi.org/10.1109/cicybs.2009.4925096","mag":"1969566588"},"language":"en","primary_location":{"id":"doi:10.1109/cicybs.2009.4925096","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cicybs.2009.4925096","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE Symposium on Computational Intelligence in Cyber Security","raw_type":"proceedings-article"},"type":"conference-paper","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/A5064203115","display_name":"Siddharth Gujral","orcid":null},"institutions":[{"id":"https://openalex.org/I117965899","display_name":"University of Hawai\u02bbi at M\u0101noa","ror":"https://ror.org/01wspgy28","country_code":"US","type":"education","lineage":["https://openalex.org/I117965899"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siddharth Gujral","raw_affiliation_strings":["Department of Electrical Engineering, University of Hawaii, Manoa, Honolulu, USA","Department of Electrical Engineering, The University of Hawaii at Manoa, Honolulu, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Hawaii, Manoa, Honolulu, USA","institution_ids":["https://openalex.org/I117965899"]},{"raw_affiliation_string":"Department of Electrical Engineering, The University of Hawaii at Manoa, Honolulu, USA","institution_ids":["https://openalex.org/I117965899"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111960428","display_name":"Estefan Ortiz","orcid":null},"institutions":[{"id":"https://openalex.org/I117965899","display_name":"University of Hawai\u02bbi at M\u0101noa","ror":"https://ror.org/01wspgy28","country_code":"US","type":"education","lineage":["https://openalex.org/I117965899"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Estefan Ortiz","raw_affiliation_strings":["Department of Electrical Engineering, University of Hawaii, Manoa, Honolulu, USA","Department of Electrical Engineering, The University of Hawaii at Manoa, Honolulu, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Hawaii, Manoa, Honolulu, USA","institution_ids":["https://openalex.org/I117965899"]},{"raw_affiliation_string":"Department of Electrical Engineering, The University of Hawaii at Manoa, Honolulu, USA","institution_ids":["https://openalex.org/I117965899"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089771083","display_name":"Vassilis L. Syrmos","orcid":null},"institutions":[{"id":"https://openalex.org/I117965899","display_name":"University of Hawai\u02bbi at M\u0101noa","ror":"https://ror.org/01wspgy28","country_code":"US","type":"education","lineage":["https://openalex.org/I117965899"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vassilis L. Syrmos","raw_affiliation_strings":["Department of Electrical Engineering, University of Hawaii, Manoa, Honolulu, USA","Department of Electrical Engineering, The University of Hawaii at Manoa, Honolulu, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Hawaii, Manoa, Honolulu, USA","institution_ids":["https://openalex.org/I117965899"]},{"raw_affiliation_string":"Department of Electrical Engineering, The University of Hawaii at Manoa, Honolulu, USA","institution_ids":["https://openalex.org/I117965899"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I117965899"],"apc_list":null,"apc_paid":null,"fwci":0.2703,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.39962845,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"99","last_page":"106"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9994000196456909,"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":0.9994000196456909,"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":0.9937999844551086,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.974399983882904,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.825516939163208},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.8212767839431763},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.7348556518554688},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6552457809448242},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.617859423160553},{"id":"https://openalex.org/keywords/laplacian-matrix","display_name":"Laplacian matrix","score":0.6081439256668091},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.5123994946479797},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5044969320297241},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.5037803053855896},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4909105896949768},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4834643006324768},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4282572269439697},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.41889238357543945},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.3459806740283966},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.13619700074195862},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.10328295826911926}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.825516939163208},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.8212767839431763},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.7348556518554688},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6552457809448242},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.617859423160553},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.6081439256668091},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.5123994946479797},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5044969320297241},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.5037803053855896},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4909105896949768},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4834643006324768},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4282572269439697},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.41889238357543945},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3459806740283966},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.13619700074195862},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.10328295826911926},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cicybs.2009.4925096","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cicybs.2009.4925096","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE Symposium on Computational Intelligence in Cyber Security","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":29,"referenced_works":["https://openalex.org/W191471982","https://openalex.org/W1516409914","https://openalex.org/W1524761913","https://openalex.org/W1556024794","https://openalex.org/W1663973292","https://openalex.org/W1965902027","https://openalex.org/W2009086942","https://openalex.org/W2011730213","https://openalex.org/W2034331023","https://openalex.org/W2115940646","https://openalex.org/W2121947440","https://openalex.org/W2125531986","https://openalex.org/W2129116669","https://openalex.org/W2129610796","https://openalex.org/W2130891992","https://openalex.org/W2132914434","https://openalex.org/W2137107348","https://openalex.org/W2141465109","https://openalex.org/W2152322845","https://openalex.org/W2157665255","https://openalex.org/W2158365839","https://openalex.org/W2165874743","https://openalex.org/W3141837825","https://openalex.org/W6607829956","https://openalex.org/W6631546530","https://openalex.org/W6633310491","https://openalex.org/W6679489070","https://openalex.org/W6683235873","https://openalex.org/W6684578312"],"related_works":["https://openalex.org/W2902850141","https://openalex.org/W2607797544","https://openalex.org/W2197112176","https://openalex.org/W2885900820","https://openalex.org/W1556852487","https://openalex.org/W2136935487","https://openalex.org/W1955548541","https://openalex.org/W2265158691","https://openalex.org/W2613055102","https://openalex.org/W2032317191"],"abstract_inverted_index":{"In":[0],"this":[1,50],"paper":[2],"we":[3,150],"present":[4],"an":[5,70,143],"unsupervised":[6],"approach":[7,138],"for":[8],"intrusion":[9,144],"detection":[10,145,159],"based":[11],"on":[12,28],"spectral":[13,17,36],"clustering":[14,18,31,97],"(SC).":[15],"Recently":[16],"has":[19],"gained":[20],"wider":[21],"application":[22,132,154],"because":[23],"of":[24,49,91,133,142,155,165],"its":[25],"promising":[26,137],"results":[27,128],"several":[29],"challenging":[30],"problems":[32],"[1].":[33],"SC":[34,134,156],"uses":[35],"graph":[37],"theory":[38],"to":[39,56,69,78,139],"form":[40,57,88],"a":[41,115,136,158],"Laplacian":[42],"matrix":[43,51],"where":[44],"the":[45,96,99,131,140,148,153,163,168],"first":[46],"k":[47],"eigenvectors":[48],"are":[52,63,84,102,109,124],"clustered":[53],"using":[54,111],"k-means":[55],"representative":[58,61,107],"clusters.":[59],"The":[60],"clusters":[62,90,108],"labeled":[64],"normal":[65],"or":[66],"anomalous":[67,92],"according":[68],"assignment":[71],"heuristic.":[72],"We":[73],"have":[74],"provided":[75],"different":[76],"techniques":[77],"detect":[79],"intrusions":[80],"(or":[81],"anomalies)":[82],"which":[83],"scattered":[85,100],"uniformly":[86],"and":[87,104,117,126],"small":[89],"data.":[93],"To":[94],"improve":[95],"results,":[98],"anomalies":[101],"detected":[103],"removed":[105],"before":[106],"formed":[110],"SC.":[112],"For":[113],"evaluation,":[114],"synthetic":[116],"real":[118],"data":[119],"set":[120],"(KDD":[121],"Cup":[122],"1999)":[123],"used":[125],"our":[127],"show":[129],"that":[130,152],"is":[135],"development":[141],"system.":[146],"From":[147],"experiments":[149],"demonstrate":[151],"yields":[157],"rate":[160,171],"(DR)":[161],"in":[162],"range":[164],"91%-100%":[166],"with":[167],"false":[169],"positive":[170],"(FPR)":[172],"being":[173],"less":[174],"than":[175],"4.5%.":[176]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
