{"id":"W1990935444","doi":"10.1109/tdsc.2011.55","title":"Persuasive Cued Click-Points: Design, Implementation, and Evaluation of a Knowledge-Based Authentication Mechanism","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":187,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Password; Usability; Persuasion; Human–computer interaction; Authentication (law); Human-computer interaction in information security; Password policy; Computer security; Cognitive password; Cued speech; Message authentication code; Persuasive technology; World Wide Web; Information security; One-time password; Cryptography; Security service; Software security assurance","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005900876,0.0008749198,0.0005875738,0.000845152,0.0004363566,0.001960997,0.002209215,0.001868662,0.003793205],"category_scores_gemma":[0.02379889,0.0005748483,0.0003674935,0.0003539584,0.0008779019,0.002312585,0.001330648,0.0009272533,0.0006867464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007930989,"about_ca_system_score_gemma":0.001300375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001230848,"about_ca_topic_score_gemma":0.0006393949,"domain_scores_codex":[0.9956082,0.002102712,0.0003445098,0.0003044025,0.001364255,0.0002758513],"domain_scores_gemma":[0.9815938,0.01186138,0.001124836,0.001702945,0.002792981,0.0009239357],"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":[0.0134956,0.01754562,0.01385687,0.003814519,0.000471227,0.001075083,0.01138669,0.02364864,0.2402976,0.0144461,0.002687486,0.6572745],"study_design_scores_gemma":[0.00710491,0.07048719,0.02839088,0.0004671603,0.001525369,0.002144748,0.002824803,0.2857817,0.5695972,0.00517472,0.02581519,0.0006860252],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7668276,0.0002590023,0.2210378,0.000275946,0.0000773088,0.004517694,0.0001315468,0.003212759,0.003660395],"genre_scores_gemma":[0.8455026,0.0001387638,0.1505921,0.0001395016,0.00001278474,0.00113331,0.00009257281,0.0001539204,0.002234466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005900876,"threshold_uncertainty_score":0.0312072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0735171356676938,"score_gpt":0.311255835327131,"score_spread":0.2377386996594372,"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."}}