{"id":"W2133585005","doi":"10.1002/sec.1155","title":"An effective behavior‐based Android malware detection system","year":2014,"lang":"en","type":"article","venue":"Security and Communication Networks","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Malware; Computer science; Android malware; Android (operating system); Naive Bayes classifier; Decision tree; Machine learning; System call; Artificial intelligence; Static analysis; Support vector machine; Malware analysis; Operating system; Data mining; Programming language","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":[0.0005797222,0.0001497262,0.000163167,0.00009317293,0.0004767207,0.0001564042,0.0006983919,0.0001583377,0.000001482713],"category_scores_gemma":[0.00002045108,0.000160511,0.00004097234,0.0002983909,0.00007978116,0.0006437608,0.0001804279,0.0003531856,0.000002526316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007575069,"about_ca_system_score_gemma":0.000007403024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005405119,"about_ca_topic_score_gemma":0.0001191306,"domain_scores_codex":[0.9985282,0.0006214015,0.0002027591,0.0003308718,0.0001346084,0.0001821995],"domain_scores_gemma":[0.998037,0.0001906785,0.0001526289,0.001378525,0.0001372263,0.0001038821],"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":[0.00006815091,0.0002272336,0.000904744,0.00007686567,0.00001463447,0.000002575852,0.0008049907,0.003630355,0.0008888166,0.0469184,0.00002752478,0.9464357],"study_design_scores_gemma":[0.000343502,0.0004451042,0.00394328,0.00006775678,0.00001460949,0.00003100971,0.00008385502,0.9836258,0.006900144,0.003288995,0.00099409,0.0002618835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02774081,0.0003110189,0.9697826,0.00005521252,0.0001066534,0.000437109,0.000001185685,0.001177864,0.000387565],"genre_scores_gemma":[0.9837321,0.00005096249,0.01568846,0.0001337655,0.00004366248,0.0003246919,0.00001201022,0.00001250377,0.000001812818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9799954,"threshold_uncertainty_score":0.6545448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003457833976353953,"score_gpt":0.2277099047449153,"score_spread":0.2242520707685613,"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."}}