{"id":"W7125589732","doi":"10.1109/icsss66939.2025.11346380","title":"Gradient Boosting Decision Trees for Real-Time Phishing Attack Prevention in Cybersecurity","year":2025,"lang":"","type":"article","venue":"","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Phishing; Boosting (machine learning); Decision tree; Robustness (evolution); Gradient boosting; Intrusion detection system; Security domain; Malware; Heuristic","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":[0.003728963,0.0009525461,0.001367703,0.001213921,0.000516116,0.001042437,0.000907974,0.001038488,0.001114159],"category_scores_gemma":[0.008316808,0.0003846087,0.0007482318,0.0009010066,0.0003715641,0.0009811103,0.0005940209,0.001598513,0.0007745348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009837839,"about_ca_system_score_gemma":0.001344819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005134688,"about_ca_topic_score_gemma":0.003745195,"domain_scores_codex":[0.9986458,0.0006719578,0.00007633317,0.0001915502,0.0002656113,0.0001487532],"domain_scores_gemma":[0.9967639,0.001917456,0.0002026999,0.0002031432,0.0007601518,0.0001525973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004729409,0.0003649265,0.007377978,0.000165916,0.0001190148,0.0001100401,0.0001009249,0.6860516,0.002805908,0.00556072,0.00755733,0.2893126],"study_design_scores_gemma":[0.000008010948,0.00004808192,0.0003433678,0.000009776141,0.00001168382,0.00001269401,0.00000738134,0.996293,0.0004998465,0.002307174,0.000454304,0.000004609989],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1961776,0.005013104,0.7874125,0.001615695,0.0004822786,0.0002462294,0.0005416737,0.002904889,0.005606043],"genre_scores_gemma":[0.8946714,0.0006657721,0.1017612,0.0003318443,0.0001637312,0.0000954919,0.0005593927,0.00007289751,0.001678293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005134688,"threshold_uncertainty_score":0.01972085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02837564076504969,"score_gpt":0.3126062310794875,"score_spread":0.2842305903144378,"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."}}