{"id":"W4360602064","doi":"10.1016/j.iotcps.2023.03.001","title":"Android malware classification using optimum feature selection and ensemble machine learning","year":2023,"lang":"en","type":"article","venue":"Internet of Things and Cyber-Physical Systems","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Malware; Computer science; Random forest; Machine learning; Support vector machine; Ensemble learning; Majority rule; Artificial intelligence; Feature selection; Android (operating system); Android malware; Perceptron; Decision tree; Classifier (UML); Data mining; Artificial neural network; Computer security; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001988026,0.0001424527,0.0002369093,0.0001299607,0.00009666385,0.0001539308,0.0001793023,0.00008678441,5.552082e-7],"category_scores_gemma":[0.00003916601,0.0001280217,0.00004369589,0.0003600869,0.0000366899,0.0005727487,0.0001887026,0.0002516243,0.000003609319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004334698,"about_ca_system_score_gemma":0.000008797354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004124597,"about_ca_topic_score_gemma":0.000003842768,"domain_scores_codex":[0.999001,0.00007283593,0.0001837945,0.0003655677,0.0002013852,0.000175368],"domain_scores_gemma":[0.9994218,0.0000771069,0.0002007094,0.0001414234,0.00009527928,0.00006364076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008269131,0.0001312861,0.005348059,0.0006298829,0.0001344457,0.00002354801,0.006176049,0.003537756,0.8619052,0.04277457,0.000917087,0.07833941],"study_design_scores_gemma":[0.0001302244,0.0001957969,0.0009291896,0.0001422828,0.000008815231,0.00007894653,0.00009895822,0.9775506,0.01865066,0.001068046,0.000995031,0.0001514017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5416564,0.0001484436,0.4570743,0.0001337766,0.0001516366,0.0001721189,0.000001316648,0.0004986662,0.0001633124],"genre_scores_gemma":[0.9947337,0.00002931436,0.004414274,0.0000184868,0.00006515146,0.00001415723,0.000003766899,0.00001471329,0.0007064546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9740129,"threshold_uncertainty_score":0.5220572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01787337049937917,"score_gpt":0.2627943389079765,"score_spread":0.2449209684085974,"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."}}