{"id":"W2031254140","doi":"10.1109/greencom-ithings-cpscom.2013.122","title":"Random Forest Classification for Detecting Android Malware","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":198,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Random forest; Computer science; Android (operating system); Malware; Machine learning; Artificial intelligence; Word error rate; The Internet; Classifier (UML); Android malware; Conditional random field; Support vector machine; Feature extraction; Decision tree; Data mining; Computer security; World Wide Web; 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":[0.0001631282,0.0001021503,0.0001110331,0.00009772419,0.0001584045,0.0001517858,0.0003972975,0.00005907038,0.0000268431],"category_scores_gemma":[0.0001719487,0.00008947022,0.00006202756,0.000207048,0.00001885762,0.0009309435,0.00008067232,0.0000701238,0.00005510247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004295215,"about_ca_system_score_gemma":0.000015968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002705561,"about_ca_topic_score_gemma":0.00002365714,"domain_scores_codex":[0.9991403,0.00002024694,0.0002019363,0.0003116828,0.0001109624,0.0002148727],"domain_scores_gemma":[0.9990311,0.00019392,0.00009400788,0.0004158412,0.0002057439,0.00005940846],"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":[0.00002770275,0.00004517396,0.0009037349,0.00004406055,0.00001450285,9.058438e-7,0.0001610665,0.0001506062,0.04545469,0.04446993,0.004363096,0.9043645],"study_design_scores_gemma":[0.001964642,0.0003323281,0.006537408,0.00002445078,0.000006478196,0.00004236551,0.000146344,0.5655546,0.2678539,0.1441305,0.0128665,0.0005404917],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005246107,0.00001589394,0.9902061,0.0005454036,0.0001427536,0.0008427303,6.266368e-7,0.001243965,0.001756433],"genre_scores_gemma":[0.6054483,0.000002920468,0.3933619,0.0001528645,0.00003540032,0.0005356664,0.000001116994,0.000008398617,0.0004534528],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.903824,"threshold_uncertainty_score":0.3648489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02155114889517997,"score_gpt":0.2638419700353418,"score_spread":0.2422908211401618,"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."}}