{"id":"W4388916527","doi":"10.1109/pst58708.2023.10320177","title":"A Comparison of Machine Learning Algorithms for Multilingual Phishing Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Phishing; Computer science; Machine learning; Harm; Artificial intelligence; Transformer; Algorithm; World Wide Web; The Internet; Engineering","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":[0.0004913367,0.00007877919,0.000144455,0.0001726458,0.0001812129,0.00007189585,0.0002500362,0.00005645578,0.000004179801],"category_scores_gemma":[0.0002314977,0.00007535861,0.00007100603,0.0005585891,0.0000126587,0.0002677119,0.00008425959,0.0001377106,0.00001553778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002083704,"about_ca_system_score_gemma":0.0000132549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002159787,"about_ca_topic_score_gemma":0.0001017004,"domain_scores_codex":[0.9991715,0.00003782512,0.0002147158,0.0002298847,0.0001705472,0.0001755538],"domain_scores_gemma":[0.9993782,0.0002482168,0.0001027031,0.0001562977,0.00007790812,0.00003669854],"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":[0.00002294231,0.00004870713,0.003027336,0.00004568595,0.00002199678,0.00000106576,0.003378228,0.006845026,0.05963733,0.0005777967,0.0001242967,0.9262696],"study_design_scores_gemma":[0.000224592,0.0001643742,0.0007766967,0.00000698397,0.000003468938,0.00000180285,0.00009365071,0.7912322,0.2053565,0.0003382369,0.001729892,0.00007159188],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1571157,0.00003712745,0.8411098,0.00009247288,0.0006806783,0.0001358026,0.000001093059,0.0006382198,0.0001891172],"genre_scores_gemma":[0.9776458,0.000003337186,0.02190136,0.00001485099,0.00009277093,0.00001536852,0.000004757888,0.000008757947,0.0003130484],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.926198,"threshold_uncertainty_score":0.3073035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06505257610855099,"score_gpt":0.3477292427535537,"score_spread":0.2826766666450027,"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."}}