{"id":"W4229005829","doi":"10.3390/app12094664","title":"Empirical Analysis of Forest Penalizing Attribute and Its Enhanced Variations for Android Malware Detection","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Malware; Android (operating system); Computer science; Android malware; Machine learning; Artificial intelligence; Mobile malware; Support vector machine; Data mining; 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":[0.0007109587,0.00009708112,0.0002075909,0.000524407,0.001098906,0.00008736275,0.000521065,0.00002856393,0.00001184349],"category_scores_gemma":[0.00007057663,0.00009665973,0.00007286388,0.003197698,0.00009925672,0.0003161353,0.0003249523,0.00008623871,4.334063e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005488846,"about_ca_system_score_gemma":0.00006348566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009238403,"about_ca_topic_score_gemma":0.00006203826,"domain_scores_codex":[0.9986582,0.00003915545,0.0002527479,0.0004817596,0.0003546354,0.0002135416],"domain_scores_gemma":[0.9991738,0.0002770326,0.000209834,0.0002013789,0.00008913863,0.00004881013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001025698,0.0002820955,0.005381993,0.00008327208,0.0004494039,0.000002333542,0.005338668,0.1148909,0.5885492,0.2117837,0.0001547338,0.07298114],"study_design_scores_gemma":[0.0004865777,0.0007503548,0.01789859,0.000005053023,0.0002090149,0.00001309386,0.0006150948,0.4528466,0.4946076,0.03057311,0.001516158,0.0004787918],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1481971,0.00004256925,0.8509572,0.0001377142,0.00006932944,0.0003026132,0.00001966077,0.0001453043,0.0001285264],"genre_scores_gemma":[0.9603471,0.000004553169,0.03913701,0.0001004047,0.00001149096,0.0003722784,0.000004197355,0.000003934482,0.00001898975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.81215,"threshold_uncertainty_score":0.8452008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03652038637443307,"score_gpt":0.3144957016320316,"score_spread":0.2779753152575985,"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."}}