{"id":"W2996826556","doi":"10.1109/access.2019.2958927","title":"Android Malware Detection Based on Factorization Machine","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Android (operating system); Malware; Android malware; Machine learning; Mobile malware; Artificial intelligence; Feature extraction; Support vector machine; Mobile device; Computer security; 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":[0.000101927,0.0001418441,0.0001208741,0.0002298937,0.00008579509,0.000194847,0.0007332977,0.00007989673,0.00004994147],"category_scores_gemma":[0.00002925017,0.0001355258,0.0000460977,0.000539194,0.0000104825,0.001287037,0.00007355776,0.0001570589,0.0001003253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009847688,"about_ca_system_score_gemma":0.00002211041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002970445,"about_ca_topic_score_gemma":0.00001580014,"domain_scores_codex":[0.9989287,0.00004545633,0.0001594201,0.0004067395,0.0002785639,0.0001810862],"domain_scores_gemma":[0.9990537,0.00006613397,0.0001148573,0.000616096,0.0000956619,0.00005358105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002205435,0.0003457502,0.02020436,0.0001848484,0.00002850583,0.00003430114,0.0001741507,0.05608129,0.1795995,0.00236372,0.0006541059,0.740109],"study_design_scores_gemma":[0.000331306,0.0002667777,0.004025549,0.00002333111,0.00000216283,0.000005044293,0.000001216635,0.2116665,0.7788955,0.002022755,0.002524918,0.0002349774],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02401938,0.0000071081,0.9723613,0.00009497462,0.001178946,0.0003099485,0.000004186257,0.0009773009,0.001046808],"genre_scores_gemma":[0.994405,0.000003660651,0.004858764,0.0004706306,0.00005808832,0.00003449332,0.000003818356,0.00001659143,0.0001489152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9703857,"threshold_uncertainty_score":0.5526583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01188737020535464,"score_gpt":0.2743346669232705,"score_spread":0.2624472967179159,"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."}}