{"id":"W4378418350","doi":"10.18280/ria.370230","title":"ConvNet Based Malicious URL Identification for Safer Use","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"SAFER; Identification (biology); Computer science; Computer security; Internet privacy; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006897115,0.0001303802,0.0001346887,0.0002188939,0.0002529459,0.00037398,0.0005989455,0.00008511038,0.00003762313],"category_scores_gemma":[0.0003187962,0.0001383306,0.0001117815,0.001019792,0.00004091262,0.0004882907,0.00006910463,0.00009721416,0.001332236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004440617,"about_ca_system_score_gemma":0.00003847788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004787732,"about_ca_topic_score_gemma":0.00001677991,"domain_scores_codex":[0.9985858,0.00005376797,0.0003739306,0.0004895843,0.0001699567,0.0003270138],"domain_scores_gemma":[0.9984155,0.0004754443,0.0001154394,0.0007459253,0.000168329,0.00007941383],"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":[0.0001179105,0.0003697061,0.001424818,0.0003297664,0.00006357046,0.00004012812,0.005081167,0.3258601,0.1271291,0.1562311,0.04944661,0.3339061],"study_design_scores_gemma":[0.00003708073,0.00006254088,0.0004617302,0.00002271291,0.000005224706,0.000004284332,0.00005175994,0.8297212,0.1403278,0.003616549,0.02553302,0.0001560994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02396406,0.00002986331,0.9720676,0.00155146,0.001262787,0.0003994858,0.00001172937,0.000534005,0.0001790197],"genre_scores_gemma":[0.9906362,0.00001929048,0.004351102,0.0003055065,0.0001291384,0.0001223366,0.00003678381,0.00001937815,0.004380233],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9677165,"threshold_uncertainty_score":0.9994453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0756388534898429,"score_gpt":0.292466342040692,"score_spread":0.2168274885508491,"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."}}