{"id":"W4291804151","doi":"10.1109/isncc55209.2022.9851790","title":"Study on Integration of FastAPI and Machine Learning for Continuous Authentication of Behavioral Biometrics","year":2022,"lang":"en","type":"article","venue":"2022 International Symposium on Networks, Computers and Communications (ISNCC)","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Biometrics; Computer science; Authentication (law); Overhead (engineering); Byte; Machine learning; Artificial intelligence; Computer security; Computer hardware; 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":[0.0007056388,0.0001452252,0.0002446637,0.0004369157,0.0003779728,0.0001117029,0.00128429,0.00003568847,0.000005635382],"category_scores_gemma":[0.00002386998,0.0001513828,0.00007451206,0.0005720314,0.0001089989,0.0001410598,0.000844268,0.0002546431,5.056393e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007190747,"about_ca_system_score_gemma":0.00001963114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001201823,"about_ca_topic_score_gemma":0.00002331998,"domain_scores_codex":[0.9982539,0.0003417147,0.0005468835,0.000325896,0.0004063883,0.0001252313],"domain_scores_gemma":[0.9979631,0.0005342978,0.0004641784,0.0007538763,0.0002312891,0.00005331384],"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":[0.0004558984,0.01276922,0.03303618,0.00008880862,0.0007413939,0.000004124525,0.1407582,0.01346798,0.004076806,0.6376478,0.001281224,0.1556723],"study_design_scores_gemma":[0.0009592468,0.001557217,0.00449374,0.00003882705,0.00002813215,0.000008829768,0.002132879,0.9877563,0.00007939799,0.000319569,0.002464641,0.0001612451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5212564,0.0008216444,0.4666081,0.006700742,0.001803388,0.002167977,0.0001295601,0.0001584175,0.0003536571],"genre_scores_gemma":[0.9973712,0.0001548829,0.00192786,0.00008302346,0.00002455367,0.0001328844,0.0001716113,0.00001277269,0.0001211996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9742883,"threshold_uncertainty_score":0.617321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04259443662302783,"score_gpt":0.2983465397605095,"score_spread":0.2557521031374817,"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."}}