{"id":"W4399415846","doi":"10.1109/jrfid.2024.3410881","title":"Enhanced Malware Prediction and Containment Using Bayesian Neural Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Radio Frequency Identification","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Malware; Computer science; Containment (computer programming); Artificial neural network; Bayesian probability; Artificial intelligence; Machine learning; Data mining; Computer security","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008034146,0.00132684,0.0008530173,0.001753418,0.0004434948,0.0007760688,0.001172505,0.001149421,0.001617072],"category_scores_gemma":[0.002826149,0.0005268395,0.0007334079,0.0006458557,0.0004062817,0.001832023,0.0008429495,0.001365593,0.000799925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009939299,"about_ca_system_score_gemma":0.001475525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01535722,"about_ca_topic_score_gemma":0.01536028,"domain_scores_codex":[0.99941,0.0001017581,0.00003475769,0.000147284,0.0002134666,0.00009263909],"domain_scores_gemma":[0.9989636,0.0005110282,0.0001316583,0.00005636424,0.0002941607,0.0000431244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003536032,0.0003341114,0.008237722,0.00018463,0.0001257196,0.000326954,0.0001361995,0.5740305,0.01050677,0.00691897,0.005219956,0.3936248],"study_design_scores_gemma":[0.000002200897,0.0000101493,0.0002584669,0.000005990355,0.000007789519,0.00001406772,0.000004370667,0.9969646,0.0008926506,0.001614237,0.0002207888,0.000004765282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0475454,0.001047094,0.9426178,0.0007211943,0.0001100693,0.00008367519,0.000407589,0.004217955,0.003249321],"genre_scores_gemma":[0.8083891,0.0008633932,0.1830066,0.0005065209,0.0001880509,0.0002050881,0.001199178,0.0001647797,0.005477319],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01535722,"threshold_uncertainty_score":0.0305357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01232250069698814,"score_gpt":0.2649862875820639,"score_spread":0.2526637868850758,"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."}}