{"id":"W2744423752","doi":"10.1111/coin.12122","title":"One‐class SVM for biometric authentication by keystroke dynamics for remote evaluation","year":2017,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Cisco Systems","keywords":"Keystroke dynamics; Computer science; Support vector machine; Biometrics; Authentication (law); Pattern recognition (psychology); Artificial intelligence; Anomaly detection; Class (philosophy); Word error rate; Identifier; Identification (biology); Data mining; Machine learning; Password; Computer security","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":[0.0008548374,0.0006224924,0.0008346913,0.0007729247,0.0003027329,0.0006203513,0.0005934439,0.0007204301,0.002198547],"category_scores_gemma":[0.001834265,0.0001396521,0.0005543322,0.0005443284,0.0001480939,0.000450938,0.0004052698,0.0007279498,0.0006983757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003780087,"about_ca_system_score_gemma":0.0004902268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003006555,"about_ca_topic_score_gemma":0.001785552,"domain_scores_codex":[0.9995164,0.0001364294,0.00004945917,0.0001129298,0.0001208637,0.00006406744],"domain_scores_gemma":[0.9991016,0.0004224682,0.00006733453,0.0001031149,0.000254941,0.00005055856],"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":[0.0007354044,0.0005061553,0.00998725,0.0001889019,0.0001215171,0.0001139279,0.00005276532,0.1920887,0.02376132,0.001256268,0.006555638,0.7646322],"study_design_scores_gemma":[0.000004424302,0.00003646195,0.001470943,0.000004709322,0.000006253831,0.00001468392,0.0000138276,0.996182,0.001853402,0.0002053726,0.0002038004,0.000004225972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4440913,0.001557349,0.5464627,0.0004670987,0.0003056069,0.0001535641,0.0006091852,0.00364977,0.002703311],"genre_scores_gemma":[0.9509104,0.0001372951,0.04696574,0.00003212913,0.00003363291,0.00005436985,0.0004482553,0.00002832747,0.001389878],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003006555,"threshold_uncertainty_score":0.007354856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09944384755894252,"score_gpt":0.3727990923741439,"score_spread":0.2733552448152014,"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."}}