{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001579863,0.0001296731,0.0001272426,0.0001095056,0.0001999712,0.00008779984,0.0002464702,0.0000715251,0.00001243272],"category_scores_gemma":[0.0001141848,0.000133222,0.00008868222,0.0002630423,0.00001182734,0.0002023908,0.00008998682,0.0001825019,0.000012036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007688825,"about_ca_system_score_gemma":0.0000490928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008674851,"about_ca_topic_score_gemma":0.00008290362,"domain_scores_codex":[0.9989573,0.00005022999,0.0001602279,0.0004302977,0.0001939137,0.0002079935],"domain_scores_gemma":[0.9992196,0.0001023581,0.00008755726,0.0003745765,0.0001590533,0.00005686315],"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":[0.000153615,0.0002361273,0.0006930227,0.0001690614,0.0000241742,0.00004354491,0.0002174581,0.5268207,0.03070059,0.003794652,0.0002173566,0.4369297],"study_design_scores_gemma":[0.0002641005,0.0001677078,0.0001079191,0.00002095396,0.000003469804,0.000007730982,0.00000464923,0.7821257,0.2097179,0.00205779,0.005393393,0.0001286283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009827107,0.00006448758,0.9884688,0.0003618477,0.0001506397,0.0001753001,0.000004222003,0.000684227,0.0002634081],"genre_scores_gemma":[0.8250691,0.00000373165,0.1736365,0.0007297919,0.00003373248,0.00007702405,0.00000563595,0.00001611857,0.0004283703],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8152421,"threshold_uncertainty_score":0.5432637,"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."}}