{"id":"W4400577032","doi":"10.5220/0012792800003767","title":"Enhancing Adversarial Defense in Behavioral Authentication Systems Through Random Projections","year":2024,"lang":"en","type":"article","venue":"","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Adversarial system; Computer science; Authentication (law); Computer security; Artificial intelligence","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":[0.0004789602,0.0001143192,0.0001558254,0.0002063675,0.00008670463,0.0005766955,0.000330264,0.00007482756,0.00002115995],"category_scores_gemma":[0.00002222294,0.000100275,0.00007573832,0.0007016089,0.0000223705,0.0008648669,0.00006798755,0.000134543,0.0002834582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000110314,"about_ca_system_score_gemma":0.0001255887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001108886,"about_ca_topic_score_gemma":0.0003480536,"domain_scores_codex":[0.9985791,0.000139861,0.0004095616,0.0003960988,0.0002585721,0.0002168219],"domain_scores_gemma":[0.9993691,0.00009101911,0.00004003405,0.0003982724,0.00005556069,0.00004594793],"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":[0.00002006291,0.0003550222,0.0004363254,0.0002355085,0.00004359024,0.00003928456,0.2687736,0.00003705149,0.005646498,0.7219173,0.001198656,0.001297166],"study_design_scores_gemma":[0.001599142,0.00008726317,0.0003865368,0.0003393854,0.00004351953,0.0001055121,0.002484775,0.9658539,0.001960528,0.003979268,0.02270533,0.0004548669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09584905,0.0005076418,0.8926876,0.0007847576,0.005043199,0.0009750744,0.000003734961,0.0008817855,0.003267147],"genre_scores_gemma":[0.9967473,0.00000855017,0.00126138,0.00002876294,0.0001114852,0.0001250551,0.000005977726,0.000009353229,0.001702123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9658168,"threshold_uncertainty_score":0.5561088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03028852012344179,"score_gpt":0.3038794362259902,"score_spread":0.2735909161025484,"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."}}