{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0008308049,0.0001676038,0.000184027,0.000206794,0.0013912,0.0001489152,0.000984918,0.0001511457,0.00001796569],"category_scores_gemma":[0.0001027918,0.0001788977,0.0001338857,0.00118027,0.00005130002,0.0002984386,0.00005643363,0.0005247982,0.00001693473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002730973,"about_ca_system_score_gemma":0.00009056545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003310609,"about_ca_topic_score_gemma":0.00009729079,"domain_scores_codex":[0.9984948,0.0002371779,0.0003231117,0.0003947809,0.0002574907,0.0002926429],"domain_scores_gemma":[0.9975551,0.0003441887,0.0001409781,0.001607752,0.0003250859,0.00002693699],"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":[0.00001212124,0.0002828015,0.00007759053,0.00005931273,0.000124865,0.000001013274,0.0007141737,0.3327312,0.0004713197,0.02906548,0.0002554019,0.6362047],"study_design_scores_gemma":[0.0004347032,0.0001775434,0.00004583724,0.00009264293,0.00006981999,0.00002349641,0.001599622,0.9407287,0.01369124,0.008226396,0.03462903,0.0002809969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001997061,0.002715057,0.9867321,0.003071571,0.0004067209,0.0006408719,0.000006458256,0.003676692,0.0007534202],"genre_scores_gemma":[0.9112611,0.000788444,0.08628487,0.00002598083,0.00001597568,0.001493031,0.00003243458,0.00001614624,0.00008205302],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.909264,"threshold_uncertainty_score":0.9999089,"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."}}