{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001436223,0.0008691326,0.0007882058,0.00267448,0.0005313539,0.001230788,0.000729104,0.0005321215,0.0006033651],"category_scores_gemma":[0.006488208,0.0002725446,0.0009907358,0.001036912,0.0004800448,0.001371042,0.001056152,0.0007841144,0.0003875644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004955265,"about_ca_system_score_gemma":0.001001885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003073454,"about_ca_topic_score_gemma":0.002504945,"domain_scores_codex":[0.9984763,0.0002558362,0.0001107223,0.0002848447,0.0007180309,0.0001542673],"domain_scores_gemma":[0.9941806,0.002834303,0.0007438867,0.0010304,0.001048043,0.0001627435],"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":[0.000558787,0.0005949368,0.04504157,0.0003064704,0.0001743375,0.0004656627,0.0007356276,0.04366953,0.1733064,0.00390522,0.002390176,0.7288513],"study_design_scores_gemma":[0.00002229328,0.0003649897,0.02157805,0.00003199523,0.0001186712,0.0005777882,0.0001379696,0.8586442,0.1115565,0.003761749,0.003142037,0.00006377463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3862404,0.0004026505,0.6035812,0.0002038898,0.00004426004,0.0002371367,0.0001648049,0.008276623,0.0008491171],"genre_scores_gemma":[0.7709846,0.0001685392,0.2276489,0.00006139308,0.00003911122,0.00007499825,0.0003017735,0.0001394198,0.0005811799],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003073454,"threshold_uncertainty_score":0.007595539,"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."}}