{"id":"W4389841240","doi":"10.5267/j.dsl.2023.12.004","title":"A machine learning technique for Android malicious attacks detection based on API calls","year":2023,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Malware; Android (operating system); Android malware; Computer science; Cryptovirology; Computer security; Software; Computer virus; Static analysis; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009576379,0.0008795722,0.0007415641,0.002617849,0.0007261823,0.0008524212,0.0008957838,0.0008334157,0.001109471],"category_scores_gemma":[0.004268522,0.0002114672,0.0008485269,0.00133221,0.0003684324,0.0008514934,0.0005059854,0.001374427,0.001034089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004433983,"about_ca_system_score_gemma":0.000892111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00306659,"about_ca_topic_score_gemma":0.003063028,"domain_scores_codex":[0.9991317,0.0001311171,0.00008229235,0.0002175741,0.0003520517,0.00008516735],"domain_scores_gemma":[0.998476,0.0006297448,0.0001634491,0.0001799522,0.0005020285,0.00004880441],"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":[0.0001675072,0.0003546941,0.009031854,0.0001574097,0.0001321531,0.0002570673,0.0001309295,0.03386448,0.01683648,0.003967877,0.006140529,0.9289591],"study_design_scores_gemma":[0.00001414784,0.0002194941,0.005316312,0.00003866524,0.00005951695,0.0004679626,0.00005715845,0.9713029,0.01370249,0.003993968,0.004792736,0.00003464367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07310285,0.001395116,0.9139531,0.0005508799,0.0002632597,0.0003062739,0.0005050946,0.005238347,0.004685069],"genre_scores_gemma":[0.582765,0.0007800314,0.4087859,0.0002208397,0.0002021327,0.000332813,0.0009297954,0.0001337879,0.005849504],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00306659,"threshold_uncertainty_score":0.006097496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01822572664888887,"score_gpt":0.308136573703998,"score_spread":0.2899108470551091,"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."}}