{"id":"W4283703245","doi":"10.5383/juspn.17.01.005","title":"Towards Performance of NLP Transformers on URL-Based Phishing Detection for Mobile Devices","year":2022,"lang":"en","type":"article","venue":"Journal of Ubiquitous Systems and Pervasive Networks","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Standards and Technology; National Science Foundation","keywords":"Phishing; Computer science; Transformer; Artificial intelligence; Machine learning; Deep learning; Mobile device; Artificial neural network; Social media; Vocabulary; World Wide Web; The Internet; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001480078,0.001321322,0.0005613126,0.001503347,0.0002597314,0.001006854,0.0009342817,0.001114668,0.002946279],"category_scores_gemma":[0.006428669,0.0003798633,0.0005843769,0.0005886155,0.0003648468,0.002590356,0.001033678,0.001063688,0.001791508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001085463,"about_ca_system_score_gemma":0.0006743839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0134257,"about_ca_topic_score_gemma":0.009635412,"domain_scores_codex":[0.9991434,0.0001989445,0.00009214146,0.0002460999,0.0001971681,0.0001222962],"domain_scores_gemma":[0.9978576,0.001280796,0.0001331556,0.0002086953,0.0004157196,0.0001040123],"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":[0.00286167,0.001025452,0.0380108,0.0008280629,0.0003327685,0.0006467129,0.0002758957,0.2018526,0.04747595,0.00253967,0.01788308,0.6862674],"study_design_scores_gemma":[0.00003651814,0.0003252142,0.003595314,0.00003980151,0.00003782452,0.0001045507,0.00009170631,0.9748021,0.01916723,0.0007892994,0.0009875105,0.0000227834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9065735,0.002778587,0.06272473,0.0007948247,0.0003217998,0.0001639837,0.002119932,0.01415269,0.01036985],"genre_scores_gemma":[0.972774,0.0004306142,0.02100176,0.0001533463,0.00003013829,0.00004537766,0.002668572,0.00009500238,0.002801233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0134257,"threshold_uncertainty_score":0.02669513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01345756467114779,"score_gpt":0.2289287359139354,"score_spread":0.2154711712427877,"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."}}