{"id":"W2908940463","doi":"10.1002/spy2.48","title":"Multimodal mobile keystroke dynamics biometrics combining fixed and variable passwords","year":2019,"lang":"en","type":"article","venue":"Security and Privacy","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Jazan University","keywords":"Password; Keystroke dynamics; Biometrics; Computer science; Keystroke logging; Computer security; Login; Leverage (statistics); Artificial intelligence; Human–computer interaction; 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":[0.001209345,0.001018623,0.001285922,0.001647651,0.0003332648,0.0009374919,0.0005068391,0.0008675163,0.002089803],"category_scores_gemma":[0.003913966,0.000267324,0.0007309626,0.001506145,0.0005765499,0.00194702,0.001773954,0.0007923843,0.001788373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003101977,"about_ca_system_score_gemma":0.0002321415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007246828,"about_ca_topic_score_gemma":0.0008549779,"domain_scores_codex":[0.9981467,0.0004986639,0.00009281787,0.000470316,0.0006163908,0.0001752129],"domain_scores_gemma":[0.9981555,0.0005034218,0.0002576943,0.0006409219,0.0003400997,0.0001023542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001734692,0.0004342913,0.03500292,0.000671992,0.0006117034,0.000731302,0.0004692473,0.06838883,0.1560249,0.006482237,0.003279567,0.7261683],"study_design_scores_gemma":[0.00004342739,0.00150112,0.05817512,0.0001370695,0.0002523134,0.004405986,0.0004065857,0.8410612,0.07362842,0.009134522,0.01102382,0.000230361],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3071537,0.001685358,0.6821974,0.000323645,0.0001444022,0.0002081603,0.0008568646,0.001908675,0.005521837],"genre_scores_gemma":[0.9372959,0.0004463608,0.05957474,0.00006707072,0.00006027002,0.00005518881,0.0004364416,0.0000410281,0.002023075],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002089803,"threshold_uncertainty_score":0.006991088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007538214047042823,"score_gpt":0.2279127090896603,"score_spread":0.2203744950426175,"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."}}