{"id":"W2944543431","doi":"10.1002/cpe.5311","title":"Identification of Android malware using refined system calls","year":2019,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"European Commission; Intel Corporation; Silicon Valley Community Foundation","keywords":"Malware; System call; Opcode; Feature selection; Computer science; Android malware; Android (operating system); Data mining; Artificial intelligence; Machine learning; Computer security; 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":[0.0002330482,0.00009727955,0.0001426403,0.00008630849,0.0001074209,0.00009587833,0.0001521626,0.00004622137,0.000001600432],"category_scores_gemma":[0.0001191187,0.00009952564,0.0000166762,0.0002632189,0.00006050226,0.001942169,0.0001022102,0.00007974463,0.00000272313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002401131,"about_ca_system_score_gemma":0.0000344215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001441934,"about_ca_topic_score_gemma":2.169222e-7,"domain_scores_codex":[0.998969,0.00007969475,0.0003363169,0.0003356724,0.0001723499,0.0001069654],"domain_scores_gemma":[0.998939,0.0001869796,0.0003794174,0.000201085,0.0002460315,0.0000474988],"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":[0.0001474502,0.0002079848,0.002308913,0.001073333,0.00005271147,0.00003605787,0.02895459,0.00200997,0.2181085,0.2051733,0.00006382073,0.5418633],"study_design_scores_gemma":[0.0009544138,0.0004744473,0.001768158,0.0003167364,0.00003457467,0.001053122,0.01047702,0.8815367,0.09820611,0.002173473,0.002376266,0.0006289909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.275643,0.0004081517,0.7232617,0.00006501452,0.0002220365,0.0001558884,0.000001553923,0.0001067797,0.0001358711],"genre_scores_gemma":[0.9761035,0.00005997411,0.02372816,0.00006083891,0.00001030749,0.00001388984,0.000001993636,0.000004139203,0.0000171888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8795267,"threshold_uncertainty_score":0.4058538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01672593922934771,"score_gpt":0.3213727862077966,"score_spread":0.3046468469784489,"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."}}