{"id":"W4312838809","doi":"10.2139/ssrn.4264058","title":"Android Malware Classification Using Optimum Feature Selection and Ensemble Machine Learning","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Malware; Android malware; Feature selection; Computer science; Machine learning; Artificial intelligence; Android (operating system); Ensemble learning; Selection (genetic algorithm); Support vector machine; 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":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001106442,0.0001457395,0.0001343458,0.0002565496,0.001305924,0.0001346854,0.0003421713,0.00005439836,0.0000101658],"category_scores_gemma":[0.00003948088,0.0001552771,0.00005306095,0.00056331,0.00001629501,0.0006214916,0.0001996376,0.002924448,0.000001318226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001395831,"about_ca_system_score_gemma":0.0005169219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002306759,"about_ca_topic_score_gemma":0.00006691366,"domain_scores_codex":[0.9979748,0.0002176321,0.0001796513,0.0003326736,0.0003116616,0.0009835873],"domain_scores_gemma":[0.9994235,0.00002988017,0.000241327,0.0001507455,0.00009131081,0.00006321352],"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":[0.0002480679,0.0002108867,0.007803256,0.00003067524,0.0002178537,0.00003689664,0.0009600132,0.0512646,0.237536,0.210297,0.0002634314,0.4911313],"study_design_scores_gemma":[0.0008936755,0.001797125,0.0006383613,0.00001601096,0.00003371884,0.01983172,0.001094457,0.792635,0.007349055,0.1630186,0.01208488,0.0006074302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06008268,0.00173169,0.9369751,0.0005894208,0.0001364796,0.0001214316,8.131217e-7,0.0002954252,0.00006694788],"genre_scores_gemma":[0.9690415,0.0007859354,0.02915634,0.00006272954,0.00008590826,0.0000144131,0.000003023353,0.0000227779,0.0008273388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9089589,"threshold_uncertainty_score":0.9999942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01144652389429386,"score_gpt":0.2489328840135334,"score_spread":0.2374863601192396,"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."}}