{"id":"W3113558124","doi":"10.1109/iit50501.2020.9298975","title":"API Security Risk Assessment Based on Dynamic ML Models","year":2020,"lang":"en","type":"article","venue":"","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Bank of Canada; McMaster University","funders":"","keywords":"Password; Computer science; Biometrics; Authentication (law); Process (computing); Machine learning; Artificial intelligence; Data mining; 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":[0.0002476477,0.0001202406,0.0001424785,0.00005026373,0.0000938365,0.0002015388,0.000707591,0.00004640417,0.00006583902],"category_scores_gemma":[0.00001838186,0.0001032522,0.00007664131,0.0002669972,0.00001505166,0.0002993325,0.0001031158,0.0001721237,0.0002079785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000462757,"about_ca_system_score_gemma":0.00007694196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004709407,"about_ca_topic_score_gemma":0.000015558,"domain_scores_codex":[0.9986364,0.0001541251,0.0002140998,0.0004164688,0.0003995517,0.0001794182],"domain_scores_gemma":[0.9990491,0.00006839403,0.00007830585,0.0005557184,0.00006032899,0.0001881442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002699261,0.001007687,0.002632967,0.0001658362,0.00008432434,0.0000287335,0.06736019,0.01043305,0.0003137095,0.8976757,0.01007358,0.01019726],"study_design_scores_gemma":[0.0002900509,0.0000805928,0.0004261006,0.000006228379,0.00000383384,4.407114e-7,0.00004142504,0.9873267,0.00004728053,0.01016809,0.001479457,0.0001298087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009095665,0.00001012325,0.9654938,0.008409931,0.0001637637,0.0002282434,0.000008148048,0.00039746,0.01619288],"genre_scores_gemma":[0.9891368,0.000004088024,0.006635553,0.004086263,0.00002026868,0.00001462685,0.000005039251,0.000007088003,0.00009033972],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9800411,"threshold_uncertainty_score":0.4210503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01980634406728478,"score_gpt":0.2617141076251157,"score_spread":0.2419077635578309,"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."}}