{"id":"https://openalex.org/W4388901706","doi":"https://doi.org/10.1109/tencon58879.2023.10322478","title":"Log-Based Anomaly Detection Using CNN Model with Parameter Entity Labeling for Improving Log Preprocessing Approach","display_name":"Log-Based Anomaly Detection Using CNN Model with Parameter Entity Labeling for Improving Log Preprocessing Approach","publication_year":2023,"publication_date":"2023-10-31","ids":{"openalex":"https://openalex.org/W4388901706","doi":"https://doi.org/10.1109/tencon58879.2023.10322478"},"language":"en","primary_location":{"id":"doi:10.1109/tencon58879.2023.10322478","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tencon58879.2023.10322478","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON)","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/A5093315886","display_name":"Thanaphit Sutthipanyo","orcid":null},"institutions":[{"id":"https://openalex.org/I60837268","display_name":"King Mongkut's University of Technology Thonburi","ror":"https://ror.org/0057ax056","country_code":"TH","type":"education","lineage":["https://openalex.org/I60837268"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Thanaphit Sutthipanyo","raw_affiliation_strings":["King Mongkut's University of Technology,Faculty of Science,Department of Mathematics,Thonburi, Bangkok,Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"King Mongkut's University of Technology,Faculty of Science,Department of Mathematics,Thonburi, Bangkok,Thailand","institution_ids":["https://openalex.org/I60837268"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093315887","display_name":"Thanadon Lamsan","orcid":null},"institutions":[{"id":"https://openalex.org/I60837268","display_name":"King Mongkut's University of Technology Thonburi","ror":"https://ror.org/0057ax056","country_code":"TH","type":"education","lineage":["https://openalex.org/I60837268"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Thanadon Lamsan","raw_affiliation_strings":["King Mongkut's University of Technology,Faculty of Science,Department of Mathematics,Thonburi, Bangkok,Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"King Mongkut's University of Technology,Faculty of Science,Department of Mathematics,Thonburi, Bangkok,Thailand","institution_ids":["https://openalex.org/I60837268"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093315888","display_name":"Woradon Thawornsusin","orcid":null},"institutions":[{"id":"https://openalex.org/I60837268","display_name":"King Mongkut's University of Technology Thonburi","ror":"https://ror.org/0057ax056","country_code":"TH","type":"education","lineage":["https://openalex.org/I60837268"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Woradon Thawornsusin","raw_affiliation_strings":["King Mongkut's University of Technology,Faculty of Science,Department of Mathematics,Thonburi, Bangkok,Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"King Mongkut's University of Technology,Faculty of Science,Department of Mathematics,Thonburi, Bangkok,Thailand","institution_ids":["https://openalex.org/I60837268"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074028033","display_name":"Wittawin Susutti","orcid":null},"institutions":[{"id":"https://openalex.org/I60837268","display_name":"King Mongkut's University of Technology Thonburi","ror":"https://ror.org/0057ax056","country_code":"TH","type":"education","lineage":["https://openalex.org/I60837268"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Wittawin Susutti","raw_affiliation_strings":["King Mongkut's University of Technology,Faculty of Science,Department of Mathematics,Thonburi, Bangkok,Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"King Mongkut's University of Technology,Faculty of Science,Department of Mathematics,Thonburi, Bangkok,Thailand","institution_ids":["https://openalex.org/I60837268"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3793,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.62927605,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"914","last_page":"919"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12127","display_name":"Software System Performance and Reliability","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/T12127","display_name":"Software System Performance and Reliability","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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9969000220298767,"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":0.9919000267982483,"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":0.8146469593048096},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6863659620285034},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6535327434539795},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6485046148300171},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.5841159820556641},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5565400719642639},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4618123769760132},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44791823625564575},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4471493065357208},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.43108710646629333},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43065422773361206},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4179016649723053},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3684922456741333},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.1092534065246582}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8146469593048096},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6863659620285034},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6535327434539795},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6485046148300171},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.5841159820556641},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5565400719642639},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4618123769760132},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44791823625564575},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4471493065357208},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.43108710646629333},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43065422773361206},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4179016649723053},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3684922456741333},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.1092534065246582},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"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/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon58879.2023.10322478","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tencon58879.2023.10322478","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.5600000023841858}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322818","display_name":"King Mongkut's University of Technology Thonburi","ror":"https://ror.org/0057ax056"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1614298861","https://openalex.org/W1832693441","https://openalex.org/W2039157918","https://openalex.org/W2094924503","https://openalex.org/W2107263349","https://openalex.org/W2141041105","https://openalex.org/W2401686019","https://openalex.org/W2583874385","https://openalex.org/W2754665629","https://openalex.org/W2767094836","https://openalex.org/W2898998129","https://openalex.org/W2947815220","https://openalex.org/W2963921497","https://openalex.org/W2963999143","https://openalex.org/W2965838158","https://openalex.org/W2993242744","https://openalex.org/W3048896729","https://openalex.org/W3099104041","https://openalex.org/W3127712067","https://openalex.org/W3159028649","https://openalex.org/W3173451065","https://openalex.org/W3199174176","https://openalex.org/W4200415345","https://openalex.org/W4205965165","https://openalex.org/W4287210060","https://openalex.org/W4288020951","https://openalex.org/W4376288669","https://openalex.org/W6712994927"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4377864969","https://openalex.org/W2972971679"],"abstract_inverted_index":{"To":[0],"build":[1],"a":[2,28,56,66,86],"reliable":[3],"system,":[4],"anomaly":[5,45,62],"detection":[6,46,63,106,155],"is":[7,48],"the":[8,13,17,30,53,77,105,112,120,127,131,146,158,176],"principal":[9],"task":[10],"for":[11,60,82],"ensuring":[12],"system's":[14],"security.":[15],"However,":[16],"complexity":[18],"of":[19,32,55,108],"systems":[20],"and":[21,35,64,79,94,116,141,165,170],"software":[22],"has":[23,37],"increased":[24],"over":[25],"time.":[26,98],"As":[27],"result,":[29],"likelihood":[31],"system":[33],"failures":[34],"vulnerabilities":[36],"also":[38],"grown.":[39],"For":[40],"this":[41],"reason,":[42],"employing":[43,85],"manual":[44],"approaches":[47],"impractical.":[49],"This":[50],"work":[51],"proposes":[52],"use":[54],"Convolutional":[57],"Neural":[58],"Network":[59],"log-based":[61],"enhances":[65],"log":[67,160],"parsing":[68,161],"method":[69],"through":[70],"parameter":[71],"entity":[72],"labeling.":[73],"We":[74],"have":[75],"chosen":[76],"ThunderBird":[78,177],"BlueGene/L":[80],"datasets":[81],"our":[83,150],"experiments,":[84],"down-sampling":[87],"technique":[88],"to":[89],"address":[90],"data":[91],"imbalance":[92],"issues":[93],"reduce":[95],"model":[96],"training":[97,114,122,133],"The":[99],"results":[100,147],"show":[101],"that":[102,149],"when":[103,173],"comparing":[104],"outcomes":[107],"models":[109,117,128],"trained":[110,118,129],"with":[111,119,130,175],"down-sampled":[113],"dataset":[115,123,134],"full":[121,132],"(without":[124],"using":[125],"down-sampling),":[126],"exhibit":[135],"higher":[136],"recall,":[137,164],"while":[138],"their":[139],"precision":[140],"specificity":[142,166],"remain":[143],"comparable.":[144],"Additionally,":[145],"indicate":[148],"approach":[151],"demonstrates":[152],"slightly":[153],"better":[154],"performance":[156],"than":[157],"previous":[159],"method.":[162],"Precision,":[163],"reach":[167],"0.9999,":[168],"0.9933,":[169],"0.9914,":[171],"respectively,":[172],"experimenting":[174],"dataset.":[178]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}