{"id":"W4389841240","doi":"10.5267/j.dsl.2023.12.004","title":"A machine learning technique for Android malicious attacks detection based on API calls","year":2023,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Malware; Android (operating system); Android malware; Computer science; Cryptovirology; Computer security; Software; Computer virus; Static analysis; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002434208,0.0002332302,0.0002152344,0.001579173,0.0008090469,0.0003274618,0.00150256,0.00009183169,0.000005598735],"category_scores_gemma":[0.001201295,0.0002134692,0.0001260666,0.004119047,0.0002175476,0.0008468976,0.0002889395,0.0003435382,0.00008423941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002479573,"about_ca_system_score_gemma":0.00005883464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001313409,"about_ca_topic_score_gemma":0.00000541881,"domain_scores_codex":[0.9968361,0.00006993739,0.0003610823,0.001039317,0.001067194,0.0006263781],"domain_scores_gemma":[0.9978218,0.0007942396,0.0001813307,0.000863052,0.0001763496,0.0001632464],"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":[0.00004910945,0.00002131628,0.00009531488,0.0000071577,0.000001470479,0.00002801516,0.00006215622,0.01670153,0.7890225,0.0002467807,0.0006499823,0.1931147],"study_design_scores_gemma":[0.0003162896,0.0003750046,0.0004308633,0.00004382328,0.000001853099,0.00002835567,0.000006195432,0.43772,0.5484128,0.002191376,0.01019931,0.0002741213],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01420037,0.00000461534,0.9806849,0.001925187,0.0004736117,0.0008068811,0.000004696403,0.001818067,0.00008165525],"genre_scores_gemma":[0.7807404,0.000006549811,0.2148849,0.003786123,0.00004935789,0.0004489458,0.000002554458,0.00002687215,0.00005432651],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7665401,"threshold_uncertainty_score":0.8705021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01822572664888887,"score_gpt":0.308136573703998,"score_spread":0.2899108470551091,"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."}}