{"id":"W4413332626","doi":"10.1016/j.procs.2025.07.163","title":"A Comparative Analysis of Machine Learning Models for Behavioral Biometric Authentication using Keystroke Dynamics","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"Keystroke dynamics; Computer science; Biometrics; Keystroke logging; Authentication (law); Artificial intelligence; Dynamics (music); Machine learning; Human–computer interaction; Computer security; Speech recognition; Password; S/KEY","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":[0.004421053,0.001447041,0.001166274,0.002105324,0.0004519596,0.001253851,0.0009145186,0.000958695,0.001227134],"category_scores_gemma":[0.008836285,0.0002484235,0.001059991,0.001105084,0.0002874756,0.001462943,0.0007300148,0.00107984,0.000681987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001336437,"about_ca_system_score_gemma":0.001062152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01312286,"about_ca_topic_score_gemma":0.009622612,"domain_scores_codex":[0.9986456,0.0005645439,0.0001181232,0.0002303466,0.0003029614,0.0001384734],"domain_scores_gemma":[0.9948673,0.003474705,0.0002840675,0.0003692828,0.0008780811,0.0001265769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001422068,0.0006645276,0.03621927,0.0004515133,0.0006054143,0.0001501173,0.0001558508,0.6197398,0.002958416,0.003201704,0.006641672,0.3277896],"study_design_scores_gemma":[0.000008351087,0.0001883072,0.003790038,0.00003283704,0.00003890584,0.00002862544,0.00004111208,0.993923,0.0008091767,0.0006989776,0.000426579,0.00001412371],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8368652,0.01413321,0.1341069,0.0018175,0.0005409618,0.0002046546,0.001945966,0.002398068,0.007987486],"genre_scores_gemma":[0.9720539,0.001466916,0.02248374,0.0001490155,0.00006734042,0.00009537507,0.002043936,0.0000517815,0.001588044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01312286,"threshold_uncertainty_score":0.02609295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06482451492050345,"score_gpt":0.3407436140180518,"score_spread":0.2759190990975484,"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."}}