{"id":"W4241905005","doi":"10.32920/ryerson.14644191.v1","title":"Inverse biometrics for keystroke dynamics","year":2021,"lang":"en","type":"preprint","venue":"","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Biometrics; Keystroke dynamics; Computer science; Keystroke logging; Process (computing); Interface (matter); Data mining; Human–computer interaction; Artificial intelligence; Computer security; Password","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":[0.0007478319,0.0006929249,0.0006801977,0.0005404804,0.000371813,0.001054391,0.0006205512,0.001330855,0.004472938],"category_scores_gemma":[0.003722533,0.0003009906,0.00112003,0.0004745568,0.001043662,0.001297271,0.0008980258,0.001769048,0.002381464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007279276,"about_ca_system_score_gemma":0.0006506476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00200913,"about_ca_topic_score_gemma":0.001281266,"domain_scores_codex":[0.9986718,0.0003580004,0.00006116173,0.0002900005,0.0005535035,0.00006563417],"domain_scores_gemma":[0.9989688,0.000410545,0.0001199858,0.0002988035,0.0001693524,0.00003241339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002535461,0.0001483523,0.003190002,0.0005189854,0.0001391117,0.0004332814,0.000392915,0.3297571,0.1175231,0.3409892,0.005634703,0.2010197],"study_design_scores_gemma":[0.000009784501,0.0001431307,0.001077284,0.00006754423,0.00002624663,0.0005390812,0.0000329647,0.9247086,0.01026899,0.04694709,0.01613062,0.00004862296],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01113755,0.001184948,0.9815111,0.0005219005,0.000199715,0.0000441891,0.0001608646,0.0004097128,0.004830072],"genre_scores_gemma":[0.7279598,0.00369997,0.245791,0.0004041761,0.000282003,0.0002674498,0.0005432441,0.0002169327,0.02083524],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004472938,"threshold_uncertainty_score":0.01496351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03810973976384259,"score_gpt":0.277893626862153,"score_spread":0.2397838870983104,"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."}}