{"id":"W4381250293","doi":"10.1109/access.2023.3287226","title":"PhishCatcher: Client-Side Defense Against Web Spoofing Attacks Using Machine Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"University of Hail","keywords":"Computer science; Phishing; Password; Computer security; Spoofing attack; Login; Web page; World Wide Web; Web application security; SQL injection; Web development; The Internet; Search engine","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":[0.0007728118,0.000782556,0.0006733991,0.0009118632,0.0005349381,0.0007316543,0.001310376,0.001629005,0.002322182],"category_scores_gemma":[0.001579492,0.0003438913,0.0003551263,0.0004447554,0.0006341127,0.001705068,0.0009636267,0.00156127,0.001834659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006344186,"about_ca_system_score_gemma":0.0008847383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002593263,"about_ca_topic_score_gemma":0.001582025,"domain_scores_codex":[0.9991164,0.00009872331,0.00003904819,0.0001326914,0.0004445836,0.0001685175],"domain_scores_gemma":[0.999162,0.0001765511,0.0000852021,0.0002293751,0.0002692221,0.00007780352],"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":[0.00136531,0.001533341,0.01098621,0.0003224702,0.0001925005,0.001012706,0.0002140967,0.05858845,0.1664865,0.009582291,0.03791834,0.7117978],"study_design_scores_gemma":[0.00008857259,0.0004803872,0.003195371,0.00002327206,0.00002432438,0.0005952048,0.00002911853,0.8616741,0.1223417,0.00225306,0.009237886,0.00005705391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2018607,0.001089537,0.6644465,0.001123502,0.0002707139,0.001109475,0.0004523745,0.117092,0.01255524],"genre_scores_gemma":[0.8569806,0.0002784349,0.1305099,0.0004736523,0.00007376474,0.0002247983,0.0008514775,0.0003556538,0.01025161],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002593263,"threshold_uncertainty_score":0.007768512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0694660670013364,"score_gpt":0.3223275751546221,"score_spread":0.2528615081532857,"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."}}