{"id":"W1999728176","doi":"10.1049/iet-ifs.2014.0099","title":"High accuracy android malware detection using ensemble learning","year":2015,"lang":"en","type":"article","venue":"IET Information Security","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":194,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Engineering and Physical Sciences Research Council","keywords":"Malware; Android malware; Computer science; Ensemble learning; Android (operating system); Machine learning; Artificial intelligence; 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":[0.0004873678,0.0001539027,0.0001496945,0.0002423256,0.0002292088,0.00029617,0.0003566247,0.0001195798,0.000008129999],"category_scores_gemma":[0.000496242,0.0001646818,0.00005047101,0.0005699432,0.00002455965,0.008710829,0.0002206496,0.0003256801,0.0001174634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002306536,"about_ca_system_score_gemma":0.00008347021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001430912,"about_ca_topic_score_gemma":0.00001208453,"domain_scores_codex":[0.9987069,0.00008077364,0.0003913339,0.0001731049,0.0004006709,0.0002471568],"domain_scores_gemma":[0.9986299,0.00005876689,0.0003418498,0.0003611994,0.0004694411,0.0001388434],"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":[0.0002125094,0.0001486615,0.0007326776,0.0002414435,0.00006157328,0.00002305382,0.032245,0.05996056,0.007879684,0.03851643,0.002326373,0.857652],"study_design_scores_gemma":[0.001214891,0.000420236,0.0003707882,0.00004849361,0.00001134415,0.000328385,0.0009733606,0.5030368,0.3459646,0.04728281,0.0995706,0.0007777085],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1210227,0.00001608394,0.8761472,0.00008159032,0.0004628094,0.0001970138,0.000002643977,0.001205492,0.0008645337],"genre_scores_gemma":[0.9520385,0.000009123284,0.04763319,0.0002082055,0.00006112899,0.00001914627,0.00001045213,0.000006928038,0.00001338542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8568743,"threshold_uncertainty_score":0.671553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02087862960961052,"score_gpt":0.2689254753340033,"score_spread":0.2480468457243928,"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."}}