{"id":"W4386394285","doi":"10.1504/ijbm.2023.133148","title":"A comprehensive study of machine learning approaches for keystroke dynamics authentication","year":2023,"lang":"en","type":"article","venue":"International Journal of Biometrics","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Keystroke dynamics; Computer science; Authentication (law); Keystroke logging; Computer security; Dynamics (music); Artificial intelligence; Machine learning; Internet privacy; Human–computer interaction; Password; S/KEY; Psychology","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":[0.0005602862,0.00008087186,0.0001922065,0.002481396,0.00004567507,0.0001125066,0.001059824,0.00003761364,0.000001985227],"category_scores_gemma":[0.0003587041,0.00007264203,0.0001150071,0.001987775,0.00002165489,0.0002409751,0.0001431613,0.0001102931,0.00000666855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008474957,"about_ca_system_score_gemma":0.00004249243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001480386,"about_ca_topic_score_gemma":0.000003988718,"domain_scores_codex":[0.9983124,0.0000805883,0.0006026044,0.0001356877,0.0007623291,0.0001063673],"domain_scores_gemma":[0.9975563,0.0003816113,0.0007041385,0.000153116,0.001151132,0.00005369681],"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":[0.0005116504,0.009578395,0.1562969,0.0005210154,0.005016632,0.0001368313,0.2427801,0.006813347,0.007833227,0.1072361,0.001652189,0.4616236],"study_design_scores_gemma":[0.0011589,0.0004873326,0.01471748,0.0000209699,0.0000214562,0.00003277509,0.002073675,0.9781769,0.0001808939,0.0009087842,0.002136319,0.00008449388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6428018,0.0001061706,0.354764,0.0007823861,0.00122447,0.0002293718,0.00002031519,0.00003675434,0.00003468725],"genre_scores_gemma":[0.9977162,0.00002906625,0.001993591,0.00001416767,0.00007151873,0.000005625233,0.00001953469,0.000007263015,0.0001430242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9713635,"threshold_uncertainty_score":0.2962256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09042635158622639,"score_gpt":0.3188703170596609,"score_spread":0.2284439654734345,"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."}}