{"id":"W3175101188","doi":"10.3390/iot2030019","title":"A Client/Server Malware Detection Model Based on Machine Learning for Android Devices","year":2021,"lang":"en","type":"article","venue":"IoT","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Android (operating system); Malware; Computer science; Mobile malware; Naive Bayes classifier; Mobile device; Mobile phone; Random forest; Machine learning; Computation; Artificial intelligence; Operating system; Support vector machine; Data mining; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000523349,0.0008875728,0.0007634571,0.0007170652,0.0004915084,0.0006328516,0.001167333,0.001079773,0.001737962],"category_scores_gemma":[0.001178936,0.0004337987,0.0009628885,0.0003600574,0.0003524793,0.0007998919,0.0003661737,0.001085734,0.0006351258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007708718,"about_ca_system_score_gemma":0.001028641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02336526,"about_ca_topic_score_gemma":0.0166722,"domain_scores_codex":[0.9996322,0.00006248585,0.00002381044,0.0001157596,0.00009117495,0.00007451794],"domain_scores_gemma":[0.9995281,0.0002156856,0.00004270468,0.0000284144,0.0001645228,0.00002060578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002315613,0.0001573353,0.004979207,0.00007789169,0.00005267531,0.0002498798,0.00008145746,0.9183945,0.004305988,0.003973166,0.001577086,0.06591932],"study_design_scores_gemma":[0.0000023648,0.000009755411,0.0001739212,0.000001756315,0.000005088457,0.00001428583,0.000001705843,0.9991096,0.0002951744,0.0002808096,0.0001022716,0.000003230197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1927432,0.00074871,0.7971042,0.0008363068,0.0001543109,0.000233448,0.0004262253,0.003315553,0.00443802],"genre_scores_gemma":[0.9170076,0.0003632834,0.07491667,0.0001253315,0.0000614514,0.0002551223,0.000361011,0.0000806768,0.006828689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02336526,"threshold_uncertainty_score":0.04645854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01755294246414554,"score_gpt":0.2684432642241041,"score_spread":0.2508903217599586,"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."}}