{"id":"W4206428177","doi":"10.1109/access.2021.3137636","title":"A Deep Learning-Based Framework for Phishing Website Detection","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Phishing; Computer science; Blacklist; Machine learning; Artificial intelligence; Personally identifiable information; Deep learning; World Wide Web; Computer security; The Internet","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002566383,0.0001144952,0.0001246747,0.00009291187,0.0003443526,0.001016576,0.000609428,0.0001317872,0.00001820537],"category_scores_gemma":[0.0003971155,0.0001244795,0.0001026821,0.0006552144,0.00001378429,0.001031468,0.00007406581,0.0002957613,0.0000237762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005857494,"about_ca_system_score_gemma":0.00005461528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003781848,"about_ca_topic_score_gemma":0.0001276848,"domain_scores_codex":[0.9988981,0.00008446259,0.000155399,0.0004134752,0.0002026862,0.0002458606],"domain_scores_gemma":[0.9989064,0.0003764773,0.0001055142,0.0003680814,0.000174916,0.00006858041],"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":[0.000143256,0.0002558361,0.006351794,0.0002692958,0.0001070965,0.00008973066,0.001609727,0.1707467,0.03086593,0.006740903,0.000639724,0.7821801],"study_design_scores_gemma":[0.0004343996,0.000095644,0.002331089,0.00006521965,0.00001724728,0.00001674896,0.00001468263,0.6945519,0.2679216,0.0247211,0.009536423,0.000293974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03324974,0.0001280584,0.9630386,0.0004878668,0.002498154,0.0001145287,4.84886e-7,0.0003418879,0.0001406666],"genre_scores_gemma":[0.9722764,0.000006293827,0.0264426,0.0006824104,0.000448876,0.00005609035,0.000002442467,0.00001585482,0.00006906605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9390266,"threshold_uncertainty_score":0.9802866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02951460665004112,"score_gpt":0.3030171318203809,"score_spread":0.2735025251703397,"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."}}