{"id":"W1916761176","doi":"10.1002/sec.1073","title":"Enhancing malware detection for Android systems using a system call filtering and abstraction process","year":2014,"lang":"en","type":"article","venue":"Security and Communication Networks","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Android (operating system); System call; Malware; Android malware; Abstraction; Anomaly detection; Malware analysis; Data mining; Machine learning; Computer security; Artificial intelligence; 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":[0.0005232742,0.000125681,0.0001652126,0.00007586538,0.0005354568,0.0002267307,0.00025166,0.0001287361,1.384612e-7],"category_scores_gemma":[0.00003411565,0.0001381102,0.00002399623,0.0001505965,0.00003793254,0.0006662774,0.0001437819,0.0002075453,2.044974e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006978957,"about_ca_system_score_gemma":0.00000922634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009476257,"about_ca_topic_score_gemma":0.0001418217,"domain_scores_codex":[0.999068,0.0001285412,0.0002672644,0.0002741311,0.0000912911,0.0001707491],"domain_scores_gemma":[0.9989093,0.0001783625,0.0002271387,0.0004735968,0.0001500032,0.00006159594],"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":[0.0004783466,0.0003118417,0.0009302206,0.009792015,0.0002390438,0.000005428609,0.01674351,0.2119418,0.040751,0.1108064,0.00007085965,0.6079295],"study_design_scores_gemma":[0.0001798768,0.00006714052,0.00007948612,0.000270333,0.000009699606,0.00009692666,0.0003089108,0.9928384,0.004247895,0.001376484,0.0003648041,0.0001600645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04740053,0.0008704652,0.950507,0.00002783768,0.0001480546,0.0004030211,0.000001013925,0.0005910495,0.00005103938],"genre_scores_gemma":[0.9852424,0.0001448689,0.01437132,0.00002289211,0.00006999314,0.0001277501,0.000002690194,0.00001504409,0.000003069238],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9378418,"threshold_uncertainty_score":0.563197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009543842832908458,"score_gpt":0.2509892719819979,"score_spread":0.2414454291490894,"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."}}