{"id":"https://openalex.org/W4210450245","doi":"https://doi.org/10.1093/jigpal/jzac007","title":"Insider attack detection in database with deep metric neural network with Monte Carlo sampling","display_name":"Insider attack detection in database with deep metric neural network with Monte Carlo sampling","publication_year":2022,"publication_date":"2022-01-28","ids":{"openalex":"https://openalex.org/W4210450245","doi":"https://doi.org/10.1093/jigpal/jzac007"},"language":"en","primary_location":{"id":"doi:10.1093/jigpal/jzac007","is_oa":false,"landing_page_url":"https://doi.org/10.1093/jigpal/jzac007","pdf_url":null,"source":{"id":"https://openalex.org/S2734381524","display_name":"Logic Journal of IGPL","issn_l":"1367-0751","issn":["1367-0751","1368-9894"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Logic Journal of the IGPL","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/A5035194461","display_name":"Gwang-Myong Go","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]},{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Gwang-Myong Go","raw_affiliation_strings":["Yonsei University Department of Computer Science, , Seoul 03722, South Korea and Samsung Electronics, Co., Ltd., Suwon 16706, South Korea"],"affiliations":[{"raw_affiliation_string":"Yonsei University Department of Computer Science, , Seoul 03722, South Korea and Samsung Electronics, Co., Ltd., Suwon 16706, South Korea","institution_ids":["https://openalex.org/I2250650973","https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112483522","display_name":"Seok-Jun Bu","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seok-Jun Bu","raw_affiliation_strings":["Yonsei University Department of Computer Science, , Seoul 03722, South Korea"],"affiliations":[{"raw_affiliation_string":"Yonsei University Department of Computer Science, , Seoul 03722, South Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108081550","display_name":"Sung\u2010Bae Cho","orcid":"https://orcid.org/0000-0002-7027-2429"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sung-Bae Cho","raw_affiliation_strings":["Yonsei University Department of Computer Science, , Seoul 03722, South Korea"],"affiliations":[{"raw_affiliation_string":"Yonsei University Department of Computer Science, , Seoul 03722, South Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5035194461"],"corresponding_institution_ids":["https://openalex.org/I193775966","https://openalex.org/I2250650973"],"apc_list":{"value":4151,"currency":"USD","value_usd":4151},"apc_paid":null,"fwci":0.9669,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.75280825,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"30","issue":"6","first_page":"979","last_page":"992"},"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/T11644","display_name":"Spam and Phishing Detection","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.9991000294685364,"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/computer-science","display_name":"Computer science","score":0.8208571672439575},{"id":"https://openalex.org/keywords/insider-threat","display_name":"Insider threat","score":0.6364985108375549},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6158367395401001},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6120107769966125},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5873533487319946},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5803576111793518},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5552006959915161},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5392275452613831},{"id":"https://openalex.org/keywords/insider","display_name":"Insider","score":0.5277621746063232},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5015387535095215},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4653196334838867},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.4359263777732849},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42071396112442017},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.07702907919883728}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8208571672439575},{"id":"https://openalex.org/C2776633304","wikidata":"https://www.wikidata.org/wiki/Q6038026","display_name":"Insider threat","level":3,"score":0.6364985108375549},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6158367395401001},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6120107769966125},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5873533487319946},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5803576111793518},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5552006959915161},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5392275452613831},{"id":"https://openalex.org/C2778971194","wikidata":"https://www.wikidata.org/wiki/Q1664551","display_name":"Insider","level":2,"score":0.5277621746063232},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5015387535095215},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4653196334838867},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.4359263777732849},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42071396112442017},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.07702907919883728},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1093/jigpal/jzac007","is_oa":false,"landing_page_url":"https://doi.org/10.1093/jigpal/jzac007","pdf_url":null,"source":{"id":"https://openalex.org/S2734381524","display_name":"Logic Journal of IGPL","issn_l":"1367-0751","issn":["1367-0751","1368-9894"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Logic Journal of the IGPL","raw_type":"journal-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/W205502958","https://openalex.org/W1481547447","https://openalex.org/W1511854050","https://openalex.org/W1562069597","https://openalex.org/W1658853941","https://openalex.org/W1852585194","https://openalex.org/W1969369024","https://openalex.org/W2014712522","https://openalex.org/W2089364633","https://openalex.org/W2098231756","https://openalex.org/W2099474155","https://openalex.org/W2150734399","https://openalex.org/W2151472800","https://openalex.org/W2154765153","https://openalex.org/W2170157775","https://openalex.org/W2471273676","https://openalex.org/W2607170611","https://openalex.org/W2766447205","https://openalex.org/W2963775347","https://openalex.org/W2976972209","https://openalex.org/W2996222779","https://openalex.org/W3081765621","https://openalex.org/W6608419203","https://openalex.org/W6630450935","https://openalex.org/W6654118001","https://openalex.org/W6682784323","https://openalex.org/W6684778303","https://openalex.org/W6783176978"],"related_works":["https://openalex.org/W2766781562","https://openalex.org/W4205304595","https://openalex.org/W2979782961","https://openalex.org/W308359497","https://openalex.org/W1499596878","https://openalex.org/W3136170567","https://openalex.org/W2947769183","https://openalex.org/W2018332730","https://openalex.org/W4387194049","https://openalex.org/W2286217954"],"abstract_inverted_index":{"Abstract":[0],"Role-based":[1],"database":[2,56],"management":[3],"systems":[4],"are":[5,15,162],"most":[6],"widely":[7],"used":[8],"for":[9,74,139],"information":[10],"storage":[11],"and":[12,57,94,109,168,171],"analysis":[13],"but":[14],"known":[16],"as":[17],"vulnerable":[18],"to":[19,66,119],"insider":[20,34],"attacks.":[21],"The":[22,160],"core":[23],"of":[24,71,100,106,149,166],"intrusion":[25],"detection":[26],"lies":[27],"in":[28,176],"an":[29,33],"adaptive":[30],"system,":[31],"where":[32],"attack":[35],"can":[36,125],"be":[37],"judged":[38],"if":[39],"it":[40,59],"is":[41,117,152,180],"different":[42],"from":[43],"the":[44,51,55,61,68,127,131,142,146,153,157,172,177],"predicted":[45],"role":[46],"by":[47,182],"performing":[48],"classification":[49,147],"on":[50],"user\u2019s":[52],"queries":[53,73,138],"accessing":[54],"comparing":[58],"with":[60,85,136,156],"authorized":[62],"role.":[63],"In":[64],"order":[65],"handle":[67],"high":[69],"similarity":[70],"user":[72],"misclassified":[75],"roles,":[76,141],"this":[77],"paper":[78],"proposes":[79],"a":[80,97,121],"deep":[81],"metric":[82],"neural":[83,178],"network":[84,179],"strategic":[86,103],"sampling":[87,104],"algorithm":[88],"that":[89,124],"properly":[90],"extracts":[91],"salient":[92],"features":[93],"directly":[95],"learns":[96],"quantitative":[98,167],"measure":[99],"similarity.":[101],"A":[102],"method":[105],"heuristically":[107],"generating":[108],"learning":[110],"training":[111,122],"pairs":[112],"through":[113,164],"Monte":[114],"Carlo":[115],"search":[116],"proposed":[118,143],"select":[120],"pair":[123],"represent":[126],"entire":[128],"dataset.":[129],"With":[130],"TPC-E\u2013based":[132],"benchmark":[133],"data":[134],"trained":[135],"11,000":[137],"11":[140],"model":[144],"produces":[145],"accuracy":[148],"95.41%,":[150],"which":[151],"highest":[154],"compared":[155],"previous":[158],"models.":[159],"results":[161],"verified":[163],"comparison":[165],"qualitative":[169],"evaluations,":[170],"feature":[173],"space":[174],"modelled":[175],"analysed":[181],"t-SNE":[183],"algorithm.":[184]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
