{"id":"W2945645805","doi":"10.1109/jsyst.2019.2906120","title":"Adversarial-Example Attacks Toward Android Malware Detection System","year":2019,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Adversarial system; Malware; Computer science; Firewall (physics); Computer security; Android malware; Android (operating system); Adversarial machine learning; Artificial intelligence; Operating system; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009537358,0.0002770488,0.000427929,0.0003740796,0.0002931216,0.0005082993,0.0009322434,0.0002047738,0.00001443977],"category_scores_gemma":[0.00002836912,0.0002545908,0.000185739,0.0004793985,0.00002327213,0.001441033,0.0001079206,0.0005740343,0.0003652333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006579047,"about_ca_system_score_gemma":0.0001099409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001517791,"about_ca_topic_score_gemma":0.000006680242,"domain_scores_codex":[0.9973351,0.0002774409,0.0006957813,0.0005138545,0.0006822653,0.0004955292],"domain_scores_gemma":[0.9979339,0.00008240409,0.0005961644,0.0007506534,0.0003981677,0.000238678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000563436,0.0003872079,0.005887596,0.003943414,0.000978152,0.001922757,0.006358357,0.1482212,0.5950511,0.01294873,0.01397079,0.2097674],"study_design_scores_gemma":[0.006832498,0.003515641,0.001704831,0.002767139,0.0001176438,0.06885448,0.002570966,0.2297949,0.4456099,0.001577969,0.2328619,0.003792187],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02410529,0.0002336475,0.959758,0.00004397877,0.01294446,0.0005471389,0.000004122252,0.001044831,0.001318563],"genre_scores_gemma":[0.9912666,0.00001889692,0.006981362,0.00003457255,0.0008689814,0.00003510277,5.771081e-7,0.0000349381,0.000758984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9671613,"threshold_uncertainty_score":0.9999906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01598094553645232,"score_gpt":0.2354679562669741,"score_spread":0.2194870107305218,"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."}}