{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004435525,0.0009406252,0.0006861936,0.002093725,0.0004624305,0.003076225,0.001454512,0.0008658107,0.002401241],"category_scores_gemma":[0.0234312,0.0004737538,0.0009108777,0.0006254474,0.001128715,0.004455823,0.002166587,0.001602901,0.000522081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001815084,"about_ca_system_score_gemma":0.001272823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002914645,"about_ca_topic_score_gemma":0.002021208,"domain_scores_codex":[0.9958892,0.00155272,0.0002383475,0.0005622329,0.001540278,0.0002171998],"domain_scores_gemma":[0.9849352,0.008723028,0.001870288,0.001818352,0.002378506,0.000274578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002917681,0.0001299568,0.01134631,0.0000797531,0.0001182834,0.0001595507,0.000192418,0.87506,0.004339361,0.04494922,0.0008611594,0.06247217],"study_design_scores_gemma":[0.000002424357,0.00002591146,0.0004019445,0.000005845402,0.000006743272,0.00002646541,0.00001080819,0.9893221,0.0006526683,0.009347374,0.0001896456,0.000008113594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07103469,0.0001257614,0.9233988,0.0003686658,0.00002046411,0.00007591687,0.0001401573,0.001114528,0.003721108],"genre_scores_gemma":[0.9455443,0.00007941024,0.05263966,0.00005550547,0.00002463867,0.0000676898,0.0001604438,0.00006577704,0.001362582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004435525,"threshold_uncertainty_score":0.02345759,"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."}}