{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004956127,0.001241058,0.0005665568,0.001432996,0.0002969579,0.0004110629,0.001145568,0.0007521771,0.00111214],"category_scores_gemma":[0.0012945,0.0003164219,0.0008359544,0.0003882062,0.0002573983,0.0009160977,0.0006320329,0.0008950245,0.0004198586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007466972,"about_ca_system_score_gemma":0.0007712711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01129193,"about_ca_topic_score_gemma":0.01704067,"domain_scores_codex":[0.9997866,0.00002883232,0.0000135196,0.00006589593,0.00007296597,0.00003232859],"domain_scores_gemma":[0.9996575,0.0001411582,0.00003338032,0.00003351628,0.0001161781,0.00001825585],"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":[0.0007001243,0.0006474066,0.01513286,0.0002978041,0.0004555236,0.0003600166,0.0001322544,0.423404,0.01485662,0.001798598,0.01587059,0.5263442],"study_design_scores_gemma":[0.00001732548,0.0001426161,0.001730791,0.00001098816,0.00002617984,0.00007156219,0.00001256909,0.9927832,0.003507453,0.0005547429,0.001124589,0.00001800294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5293843,0.002702219,0.4352176,0.001112323,0.000442875,0.0007930478,0.003263604,0.02011808,0.006965931],"genre_scores_gemma":[0.8309403,0.0006603169,0.1545774,0.000414084,0.0001111599,0.0005111665,0.004045678,0.0001866621,0.008553395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01129193,"threshold_uncertainty_score":0.02245241,"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."}}