{"id":"W2803049783","doi":"10.1109/isdfs.2018.8355360","title":"Android gaming malware detection using system call analysis","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University of Edmonton","funders":"Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Malware; Android (operating system); Computer science; System call; Android malware; Malware analysis; Static analysis; Computer security; Taint checking; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002140424,0.0001304086,0.0001859874,0.0004816616,0.0002538754,0.0001176577,0.0003840884,0.00007527616,0.00001393078],"category_scores_gemma":[0.00003083245,0.000123743,0.00009230335,0.002379211,0.00004132328,0.0006251454,0.0001814498,0.00008550678,0.00002967804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002315092,"about_ca_system_score_gemma":0.00002085937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002010387,"about_ca_topic_score_gemma":0.0002101612,"domain_scores_codex":[0.998813,0.00005396099,0.0002362777,0.0004363556,0.0002182232,0.0002421554],"domain_scores_gemma":[0.998876,0.00002891253,0.0001170175,0.0005945305,0.0003083333,0.00007522276],"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":[0.00007385787,0.0001489458,0.006052188,0.0002600862,0.00122063,0.0001658674,0.001773993,0.005325863,0.4027078,0.02318056,0.0002338059,0.5588564],"study_design_scores_gemma":[0.00006910584,0.00009029789,0.0005606532,0.00001355729,0.00005475637,0.0000705061,0.00005468971,0.6431164,0.3551885,0.0001346908,0.0004576283,0.0001892137],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0369086,0.00001443648,0.9592576,0.00001265352,0.0002355121,0.0001102023,7.533571e-7,0.001849833,0.001610395],"genre_scores_gemma":[0.7732804,7.878569e-7,0.2264271,0.00004538805,0.0000828351,0.000007714657,3.272396e-7,0.000007692739,0.0001476571],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7363719,"threshold_uncertainty_score":0.5046094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01320564960944947,"score_gpt":0.2644398467101675,"score_spread":0.251234197100718,"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."}}