{"id":"W4205211063","doi":"10.1109/rivf51545.2021.9642094","title":"MalDuoNet: A DualNet Framework to Detect Android Malware","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Malware; Computer science; Android (operating system); Classifier (UML); Popularity; Android malware; Mobile device; Machine learning; Artificial intelligence; Data mining; Computer security; World Wide Web; 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":[0.0001227877,0.0001637919,0.0001771639,0.0001276967,0.0001072794,0.0001676903,0.0006007627,0.0001075293,0.0003182668],"category_scores_gemma":[0.0002753643,0.0001643067,0.00007087137,0.001072576,0.00001681139,0.0003643201,0.0005854081,0.000218051,0.0002927019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005841804,"about_ca_system_score_gemma":0.00006156132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009706342,"about_ca_topic_score_gemma":0.00002827714,"domain_scores_codex":[0.9985196,0.00005431431,0.0002112636,0.0005979982,0.000273919,0.0003429151],"domain_scores_gemma":[0.9984732,0.0001228889,0.0000423512,0.001001626,0.0001731957,0.0001867735],"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":[0.00002827241,0.00009890519,0.0003811389,0.00004792076,0.00004575436,0.0008771964,0.0007309085,0.0004070748,0.03524117,0.1798259,0.02181356,0.7605022],"study_design_scores_gemma":[0.0001418345,0.0002246101,0.001051014,0.00005493376,0.000003791442,0.0003110966,0.00005213471,0.0009334965,0.7566277,0.1269646,0.1131178,0.0005169812],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002145169,0.0001234849,0.9900602,0.00168347,0.0002888848,0.0001627488,0.000002831342,0.001612909,0.003920337],"genre_scores_gemma":[0.149241,0.00001839305,0.8450263,0.003634524,0.00006421743,0.00005646432,9.671576e-7,0.00001647109,0.001941674],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7599852,"threshold_uncertainty_score":0.6700233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01054178793131649,"score_gpt":0.2684818674546844,"score_spread":0.2579400795233679,"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."}}