{"id":"W2999770140","doi":"10.1109/pst47121.2019.8949031","title":"User Authentication Using Keystroke Dynamics via Crowdsourcing","year":2019,"lang":"en","type":"article","venue":"","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Keystroke dynamics; Authentication (law); Computer science; Computer security; Password; Keystroke logging; Vulnerability (computing); Crowdsourcing; Challenge–response authentication; Multi-factor authentication; Authentication protocol; World Wide Web; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002412102,0.00009844276,0.0001190707,0.0001100368,0.00007541554,0.0002274353,0.0005749541,0.00005528044,0.00009433804],"category_scores_gemma":[0.000009081849,0.00009190473,0.00005769096,0.0002563916,0.00001341282,0.0004923017,0.0001334707,0.00007262429,0.000777939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009150863,"about_ca_system_score_gemma":0.00003105083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009174425,"about_ca_topic_score_gemma":0.00001877748,"domain_scores_codex":[0.9989651,0.00005021272,0.0002364012,0.0002981206,0.0002460334,0.000204118],"domain_scores_gemma":[0.9990422,0.00003264138,0.00008957813,0.0006803811,0.0000833333,0.00007182991],"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":[0.000006360638,0.0002660071,0.09346016,0.0001479782,0.00008689454,0.000004032129,0.03731333,0.0001894172,0.04835591,0.8106853,0.0002727959,0.009211826],"study_design_scores_gemma":[0.0001427632,0.00001083549,0.003291662,0.00001272714,0.000004220164,0.00001252907,0.00009483624,0.9939098,0.0006954328,0.0008505099,0.0008484914,0.0001261373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3880272,0.000007680318,0.6093822,0.0002948857,0.000376257,0.0001470564,4.790344e-7,0.0001424535,0.001621868],"genre_scores_gemma":[0.9820465,4.736953e-7,0.01299428,0.0001728778,0.00002665053,0.000002952289,0.000003457065,0.00000905354,0.004743724],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9937204,"threshold_uncertainty_score":0.9999091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01029843864601378,"score_gpt":0.2347794577330017,"score_spread":0.2244810190869879,"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."}}