{"id":"W4236057182","doi":"10.1109/trustcom.2015.466","title":"CaptureMe: Attacking the User Credential in Mobile Banking Applications","year":2015,"lang":"en","type":"article","venue":"2015 IEEE Trustcom/BigDataSE/ISPA","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Credential; Password; Computer science; Android (operating system); Optical character recognition; Computer security; Mobile device; Mobile banking; Internet privacy; World Wide Web; Artificial intelligence; Image (mathematics); Operating system","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009860463,0.0003400665,0.0003206755,0.0002822442,0.0002785171,0.0003282539,0.002354064,0.0001691685,0.00006523036],"category_scores_gemma":[0.00007770922,0.0002861505,0.00009786036,0.001088681,0.0001788786,0.001426829,0.0005671157,0.0005843309,0.0003261812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000281548,"about_ca_system_score_gemma":0.0001691086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003176045,"about_ca_topic_score_gemma":0.000383728,"domain_scores_codex":[0.997166,0.0001941527,0.0005506409,0.0007935399,0.000680593,0.0006150129],"domain_scores_gemma":[0.9971375,0.0001496303,0.000271015,0.002009626,0.0002163733,0.0002157798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001518796,0.001049153,0.002679013,0.0001435787,0.0001646916,0.0003071297,0.004631172,0.02615037,0.00824082,0.05366695,0.6954641,0.2073512],"study_design_scores_gemma":[0.001238193,0.0001443078,0.0004149536,0.00006620691,0.00003328805,0.0001626983,0.0003023655,0.01018235,0.03160283,0.01437569,0.9405694,0.0009076927],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002435137,0.0005633243,0.9921477,0.0007478306,0.0009733421,0.001253437,0.0001371466,0.0008953891,0.0008467233],"genre_scores_gemma":[0.9137415,0.00008468987,0.08078945,0.001389321,0.000836008,0.00222593,0.0002107497,0.00009838648,0.000623988],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9113582,"threshold_uncertainty_score":0.9999591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03373599341119791,"score_gpt":0.3176770894633717,"score_spread":0.2839410960521738,"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."}}