{"id":"W2158063174","doi":"10.1109/tnn.2011.2161999","title":"Textual and Visual Content-Based Anti-Phishing: A Bayesian Approach","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":197,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Phishing; Computer science; Naive Bayes classifier; Artificial intelligence; Classifier (UML); Web page; Machine learning; Bayes classifier; Pattern recognition (psychology); Data mining; The Internet; Support vector machine; World Wide Web","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":[0.003855479,0.001169644,0.001791411,0.005584873,0.0009887354,0.003090116,0.002945619,0.002558383,0.00235294],"category_scores_gemma":[0.01580127,0.001226585,0.001427817,0.002460018,0.001916504,0.004796457,0.001872974,0.002177449,0.001334714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00200529,"about_ca_system_score_gemma":0.001924285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01022481,"about_ca_topic_score_gemma":0.01038655,"domain_scores_codex":[0.9970386,0.0008698393,0.0001893404,0.0005350437,0.001151774,0.0002154946],"domain_scores_gemma":[0.9937032,0.003265476,0.0007501512,0.0004916867,0.001539639,0.0002497716],"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":[0.0004646776,0.0007790254,0.01591803,0.0006122381,0.0004584243,0.0005075514,0.001222028,0.2998356,0.01718642,0.1127141,0.006139331,0.5441627],"study_design_scores_gemma":[0.00001808386,0.00008083443,0.002410674,0.00007697182,0.00006813858,0.000205456,0.00007970851,0.9457555,0.001948621,0.04708703,0.002206068,0.00006295259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00791001,0.0004718778,0.9887326,0.0005021408,0.00003182602,0.00008364209,0.0001037859,0.0003503232,0.001813765],"genre_scores_gemma":[0.4866054,0.001323424,0.5043105,0.0004877223,0.0005401519,0.0004566381,0.0005159996,0.0001729789,0.005587179],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01022481,"threshold_uncertainty_score":0.02038997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04666925283207304,"score_gpt":0.2297973862758755,"score_spread":0.1831281334438024,"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."}}