{"id":"W3127346584","doi":"10.1002/ett.4226","title":"Machine learning for mobile network payment security evaluation system","year":2021,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Mobile payment; Computer security; Authentication (law); Multi-factor authentication; Computer network; Malware; Payment system; Mutual authentication; Payment; Mobile computing; Random oracle; Authentication protocol; World Wide Web; Public-key cryptography; Encryption","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":[0.001625795,0.000696155,0.0006931442,0.001536344,0.0005469089,0.0009803118,0.0007022521,0.0007870499,0.004710808],"category_scores_gemma":[0.002951688,0.0001338403,0.0004325206,0.0006574099,0.0002026039,0.000954052,0.0005537278,0.0005545957,0.001533842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001270958,"about_ca_system_score_gemma":0.0007945278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003487593,"about_ca_topic_score_gemma":0.001539089,"domain_scores_codex":[0.9985007,0.0003747365,0.0001532759,0.0002708706,0.0005127178,0.0001876742],"domain_scores_gemma":[0.9987519,0.0002359973,0.0001127085,0.0001079969,0.0007235162,0.00006783215],"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":[0.003008672,0.001639885,0.06094642,0.0004824653,0.0002073206,0.0009632244,0.0001582221,0.1531728,0.02758715,0.006344167,0.03251228,0.7129773],"study_design_scores_gemma":[0.00004610099,0.0002972698,0.005552093,0.00001487928,0.00002463028,0.0001304418,0.00002125301,0.9817909,0.009077981,0.0008627075,0.002158644,0.00002306014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5490759,0.00122593,0.3991519,0.001822246,0.0005573744,0.001818792,0.002877485,0.0259345,0.01753582],"genre_scores_gemma":[0.9610242,0.0001539645,0.03335004,0.000155088,0.0000392546,0.0003380529,0.001225558,0.00003254274,0.003681274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004710808,"threshold_uncertainty_score":0.01575929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02340665060076894,"score_gpt":0.2821563512428161,"score_spread":0.2587497006420471,"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."}}