{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001190925,0.0001509363,0.0001844357,0.0002722653,0.0006939395,0.0007376693,0.001527647,0.00009222539,0.000009125249],"category_scores_gemma":[0.0009659498,0.0001730204,0.0001258063,0.0002790844,0.0000954311,0.0005846597,0.0001219258,0.00006731859,0.00006552328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002166726,"about_ca_system_score_gemma":0.0001573872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002811672,"about_ca_topic_score_gemma":0.00001552703,"domain_scores_codex":[0.9980527,0.000063315,0.0004821713,0.0005434369,0.0006000116,0.000258431],"domain_scores_gemma":[0.9967828,0.0005701656,0.0004848574,0.000759892,0.001297146,0.0001051801],"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":[0.00002615887,0.0002557805,0.0001756587,0.0001410948,0.00009294728,1.559501e-7,0.003045779,0.002210372,0.0003565453,0.4602485,0.002179405,0.5312676],"study_design_scores_gemma":[0.0002072927,0.00006702218,0.001067503,0.0000260534,0.00002192762,0.000001851016,0.00002556496,0.8339859,0.0007143556,0.1621214,0.001597564,0.0001636418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004021697,0.0001286791,0.9896989,0.003549385,0.0008058595,0.001383549,0.0001012426,0.00008885402,0.0002217795],"genre_scores_gemma":[0.9281219,0.000006253874,0.07074112,0.0001206892,0.00008281531,0.000142466,0.0003238687,0.00001419468,0.0004467292],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9241002,"threshold_uncertainty_score":0.7113362,"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."}}