{"id":"https://openalex.org/W2899259623","doi":"https://doi.org/10.1145/3279996.3280013","title":"Network intrusion detection through online transformation of eigenvector reflecting concept drift","display_name":"Network intrusion detection through online transformation of eigenvector reflecting concept drift","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2899259623","doi":"https://doi.org/10.1145/3279996.3280013","mag":"2899259623"},"language":"en","primary_location":{"id":"doi:10.1145/3279996.3280013","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3279996.3280013","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the First International Conference on Data Science, E-learning and Information Systems","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/A5102774223","display_name":"Seongchul Park","orcid":"https://orcid.org/0000-0001-9908-0810"},"institutions":[{"id":"https://openalex.org/I205490536","display_name":"Dongguk University","ror":"https://ror.org/057q6n778","country_code":"KR","type":"education","lineage":["https://openalex.org/I205490536"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Seongchul Park","raw_affiliation_strings":["Dongguk University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Dongguk University, Seoul, Korea","institution_ids":["https://openalex.org/I205490536"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102997534","display_name":"Sanghyun Seo","orcid":"https://orcid.org/0000-0002-9340-5861"},"institutions":[{"id":"https://openalex.org/I205490536","display_name":"Dongguk University","ror":"https://ror.org/057q6n778","country_code":"KR","type":"education","lineage":["https://openalex.org/I205490536"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sanghyun Seo","raw_affiliation_strings":["Dongguk University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Dongguk University, Seoul, Korea","institution_ids":["https://openalex.org/I205490536"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017051247","display_name":"Changhoon Jeong","orcid":"https://orcid.org/0009-0000-6186-7837"},"institutions":[{"id":"https://openalex.org/I205490536","display_name":"Dongguk University","ror":"https://ror.org/057q6n778","country_code":"KR","type":"education","lineage":["https://openalex.org/I205490536"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Changhoon Jeong","raw_affiliation_strings":["Dongguk University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Dongguk University, Seoul, Korea","institution_ids":["https://openalex.org/I205490536"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100710223","display_name":"Juntae Kim","orcid":"https://orcid.org/0000-0003-3344-4591"},"institutions":[{"id":"https://openalex.org/I205490536","display_name":"Dongguk University","ror":"https://ror.org/057q6n778","country_code":"KR","type":"education","lineage":["https://openalex.org/I205490536"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Juntae Kim","raw_affiliation_strings":["Dongguk University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Dongguk University, Seoul, Korea","institution_ids":["https://openalex.org/I205490536"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102774223"],"corresponding_institution_ids":["https://openalex.org/I205490536"],"apc_list":null,"apc_paid":null,"fwci":0.3691,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.6536982,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9851999878883362,"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":0.9851999878883362,"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/T10057","display_name":"Face and Expression Recognition","score":0.9824000000953674,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9794999957084656,"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/principal-component-analysis","display_name":"Principal component analysis","score":0.7360409498214722},{"id":"https://openalex.org/keywords/multinomial-logistic-regression","display_name":"Multinomial logistic regression","score":0.7192663550376892},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.6885442733764648},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.6622117757797241},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6466600298881531},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.5825826525688171},{"id":"https://openalex.org/keywords/online-and-offline","display_name":"Online and offline","score":0.4976990520954132},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.492938756942749},{"id":"https://openalex.org/keywords/data-transformation","display_name":"Data transformation","score":0.46561312675476074},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45813703536987305},{"id":"https://openalex.org/keywords/multinomial-distribution","display_name":"Multinomial distribution","score":0.44860509037971497},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42694026231765747},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3572372496128082},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23481711745262146},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19624385237693787},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.17682313919067383}],"concepts":[{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.7360409498214722},{"id":"https://openalex.org/C117568660","wikidata":"https://www.wikidata.org/wiki/Q1650843","display_name":"Multinomial logistic regression","level":2,"score":0.7192663550376892},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.6885442733764648},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.6622117757797241},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6466600298881531},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.5825826525688171},{"id":"https://openalex.org/C2780102126","wikidata":"https://www.wikidata.org/wiki/Q10928179","display_name":"Online and offline","level":2,"score":0.4976990520954132},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.492938756942749},{"id":"https://openalex.org/C150670458","wikidata":"https://www.wikidata.org/wiki/Q4272815","display_name":"Data transformation","level":3,"score":0.46561312675476074},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45813703536987305},{"id":"https://openalex.org/C192065140","wikidata":"https://www.wikidata.org/wiki/Q1147928","display_name":"Multinomial distribution","level":2,"score":0.44860509037971497},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42694026231765747},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3572372496128082},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23481711745262146},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19624385237693787},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.17682313919067383},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C135572916","wikidata":"https://www.wikidata.org/wiki/Q193351","display_name":"Data warehouse","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3279996.3280013","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3279996.3280013","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the First International Conference on Data Science, E-learning and Information Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4230640685","display_name":null,"funder_award_id":"NRF-2015R1D1A1A01057078, NRF-2018R1D1A1B07049988, NRF-2017M3C4A7083279","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W5731987","https://openalex.org/W1865760297","https://openalex.org/W2017641901","https://openalex.org/W2033587694","https://openalex.org/W2131329059","https://openalex.org/W2141681031","https://openalex.org/W2150026392","https://openalex.org/W2166353797","https://openalex.org/W2217442075","https://openalex.org/W2272817427","https://openalex.org/W2763148304","https://openalex.org/W4299888561","https://openalex.org/W6639299341"],"related_works":["https://openalex.org/W2380927352","https://openalex.org/W3178621026","https://openalex.org/W2367227827","https://openalex.org/W2137598809","https://openalex.org/W2085553065","https://openalex.org/W2099082374","https://openalex.org/W4211209597","https://openalex.org/W3048981730","https://openalex.org/W2114966906","https://openalex.org/W2093715904"],"abstract_inverted_index":{"Recently,":[0],"large":[1],"amount":[2],"data":[3,13],"streams":[4],"are":[5,80],"increasing.":[6],"It":[7],"is":[8,30,46],"difficult":[9],"to":[10,33,41],"continuously":[11],"store":[12],"and":[14,94,118],"perform":[15],"the":[16,35,43,47,50,59,65,70,92,103,109,122],"principal":[17,51],"component":[18,52],"analysis":[19],"in":[20,69,100,116],"periodical":[21],"offline":[22,75,95],"(batch)":[23],"mode.":[24,76],"To":[25],"solve":[26],"this":[27,55],"problem,":[28],"there":[29],"a":[31,83,120],"need":[32],"reflect":[34],"concept":[36],"drift":[37],"through":[38,82],"online":[39,62,66,93,110],"transformation":[40,68,112],"obtain":[42],"eigenvector,":[44],"which":[45],"goal":[48],"of":[49,61,78],"analysis.":[53],"In":[54],"study,":[56],"we":[57],"compared":[58],"performance":[60,99,115],"mode":[63,96],"using":[64],"eigenvector":[67,111],"network":[71],"intrusion":[72],"detection":[73],"with":[74,108],"Both":[77],"them":[79],"applied":[81,107],"multinomial":[84,104],"logistic":[85,105],"regression(MLR).":[86],"The":[87],"results":[88],"showed":[89,113],"that":[90],"both":[91],"demonstrated":[97],"excellent":[98],"accuracy,":[101],"but":[102],"regression":[106],"better":[114],"recall":[117],"as":[119],"result,":[121],"F1-Measure":[123],"was":[124],"also":[125],"better.":[126]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}