{"id":"W2995175434","doi":"10.1109/iemcon.2019.8936183","title":"User Modeling via Anomaly Detection Techniques for User Authentication","year":2019,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Anomaly detection; Computer science; Intrusion detection system; User modeling; Data mining; Filter (signal processing); Variety (cybernetics); Authentication (law); Big data; Data modeling; Real-time computing; User interface; Artificial intelligence; Database; Computer security","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":[0.0001948076,0.0001090682,0.0001016779,0.0001286955,0.0001365739,0.0001093028,0.0003980881,0.00009513411,0.00002940977],"category_scores_gemma":[0.000006036103,0.0001018496,0.00008754559,0.0002782462,0.00001006005,0.0005368554,0.00008371081,0.00007014711,0.00009156363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005082537,"about_ca_system_score_gemma":0.00001723146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005583681,"about_ca_topic_score_gemma":0.00001159843,"domain_scores_codex":[0.9990963,0.00001347906,0.0002211812,0.000376544,0.0001153773,0.0001771306],"domain_scores_gemma":[0.9991269,0.00002692271,0.00007357141,0.0005738895,0.0001539902,0.00004479675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001911991,0.0001655644,0.0004881763,0.00004722576,0.00002912813,1.69421e-7,0.0001642905,0.0003997394,0.4028846,0.3213203,0.0004788687,0.2740028],"study_design_scores_gemma":[0.00007381037,0.00009069472,0.0001071053,0.000004701256,0.000004896966,0.000004683738,0.000008648888,0.7280136,0.2490736,0.009233459,0.01323958,0.0001451711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0242243,0.000006298217,0.9718809,0.0003283927,0.00008149965,0.0008508801,9.335802e-7,0.00122151,0.001405289],"genre_scores_gemma":[0.7470115,0.000003571614,0.2501943,0.0001575848,0.00003581164,0.0003613193,0.000002013642,0.00001127729,0.002222674],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7276139,"threshold_uncertainty_score":0.4153308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01158731071415135,"score_gpt":0.2521150418193131,"score_spread":0.2405277311051618,"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."}}