{"id":"W3040197085","doi":"10.1016/j.icte.2020.06.003","title":"Unsupervised log message anomaly detection","year":2020,"lang":"en","type":"article","venue":"ICT Express","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Autoencoder; Anomaly detection; Anomaly (physics); Isolation (microbiology); Computer science; Artificial intelligence; Data mining; Pattern recognition (psychology); Feature (linguistics); Deep learning; Bioinformatics; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005978635,0.0001043093,0.000104851,0.00004006264,0.0001449237,0.0001109749,0.0006374295,0.000063439,0.0000434147],"category_scores_gemma":[0.00001243164,0.0001033402,0.00006664043,0.0003842842,0.00002388186,0.0003371588,0.0001738183,0.0001172745,0.0001179023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001690738,"about_ca_system_score_gemma":0.00001449114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002726166,"about_ca_topic_score_gemma":0.000002089061,"domain_scores_codex":[0.9991676,0.00003283755,0.0001591416,0.0003369025,0.0001406214,0.0001629209],"domain_scores_gemma":[0.9993458,0.00002339049,0.00005609261,0.0004073904,0.00004494425,0.0001223721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002374603,0.0001221933,0.0004616388,0.00004349489,0.00003149367,0.00001989005,0.001767823,0.0001637968,0.7530859,0.03005962,0.004540412,0.20968],"study_design_scores_gemma":[0.0003399178,0.0001944547,0.002017702,0.000009093803,0.000008623241,0.00001337125,0.0000519643,0.07923293,0.7255469,0.001870333,0.1903563,0.0003583888],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02301688,0.0000490372,0.9702455,0.001529676,0.00006061279,0.0001788369,0.000002996922,0.0009516591,0.003964743],"genre_scores_gemma":[0.9806872,0.00001121749,0.01800042,0.0009581982,0.0001002929,0.00009085869,0.000001184385,0.0000100362,0.0001405927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9576703,"threshold_uncertainty_score":0.421409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01949720597244098,"score_gpt":0.2290677601710361,"score_spread":0.2095705541985951,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}