{"id":"W3215024597","doi":"10.3390/electronics10232948","title":"BrainShield: A Hybrid Machine Learning-Based Malware Detection Model for Android Devices","year":2021,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Malware; Android (operating system); Computer science; Android malware; Static analysis; Artificial neural network; Artificial intelligence; Machine learning; Android application; Mobile device; Data mining; Operating system","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":[0.0002192606,0.0001844761,0.0001808757,0.0001177641,0.0002794753,0.0001206112,0.0003798474,0.00007849061,0.000006731598],"category_scores_gemma":[0.0001897149,0.0002052953,0.0001223375,0.0003852927,0.00001772462,0.0003732593,0.00009516542,0.0003819385,0.000004933099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000191144,"about_ca_system_score_gemma":0.0003099172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002777623,"about_ca_topic_score_gemma":0.0003974325,"domain_scores_codex":[0.9985365,0.00006348515,0.0002141624,0.0005258153,0.0002034214,0.0004566749],"domain_scores_gemma":[0.9990014,0.0001237923,0.0001263646,0.0004387676,0.000236424,0.00007318988],"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":[0.0001975181,0.000329245,0.0001670375,0.0002613959,0.00009974458,0.0000709649,0.0003307621,0.1906742,0.06920984,0.01215805,0.0009812964,0.72552],"study_design_scores_gemma":[0.0002421856,0.0002108061,0.000004178979,0.000006936992,0.00000546905,0.00003655542,0.000002161581,0.622358,0.3347524,0.006974014,0.03524955,0.000157781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003041865,0.002447747,0.9923357,0.0007201408,0.00008990964,0.0002376825,0.000008820939,0.001055728,0.00006238727],"genre_scores_gemma":[0.8766943,0.0001064182,0.1214179,0.0008357205,0.00004560863,0.0001447619,0.00002874843,0.00003408728,0.0006925032],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8736524,"threshold_uncertainty_score":0.8371698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01143524120863386,"score_gpt":0.2509902535775356,"score_spread":0.2395550123689017,"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."}}