{"id":"W3047752424","doi":"10.1109/cns48642.2020.9162317","title":"Machine Learning in Action: Securing IAM API by Risk Authentication Decision Engine","year":2020,"lang":"en","type":"article","venue":"","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Bank of Canada","funders":"","keywords":"Computer science; Login; Authentication (law); Classifier (UML); Identity theft; Application programming interface; Machine learning; Computer security; Artificial intelligence; Operating system","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":[0.0003274666,0.0001082588,0.0001319154,0.00009187373,0.00009801424,0.0001546723,0.0004463729,0.00005281034,0.00008493311],"category_scores_gemma":[0.0001533095,0.0001015868,0.00004302051,0.0005164574,0.000009672618,0.0004212396,0.000127114,0.0002502405,0.0002665003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003661392,"about_ca_system_score_gemma":0.00001723515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009298705,"about_ca_topic_score_gemma":0.00002728932,"domain_scores_codex":[0.9987794,0.0001134859,0.000304244,0.0003610321,0.0002714088,0.0001703962],"domain_scores_gemma":[0.9993705,0.0001040004,0.000101279,0.0002595999,0.00003823271,0.000126392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009437376,0.0007217125,0.07859617,0.0002318109,0.0001052116,0.00002345019,0.3039021,0.001728011,0.01691125,0.03909316,0.008918501,0.5496742],"study_design_scores_gemma":[0.0003712074,0.00003661955,0.001991102,0.0000137058,0.000003197214,0.000002974521,0.00009793307,0.9618129,0.001547342,0.0005380998,0.03345766,0.0001273326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1497366,0.0002137454,0.8445125,0.004217263,0.000210799,0.0001865108,0.000002060987,0.0004226403,0.0004978233],"genre_scores_gemma":[0.995896,0.00007995788,0.00327367,0.0002895336,0.00004400367,0.00000970939,0.000009729556,0.000008448987,0.000388965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9600848,"threshold_uncertainty_score":0.4142591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01980585035984571,"score_gpt":0.2485562895371229,"score_spread":0.2287504391772772,"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."}}