{"id":"W3114490715","doi":"10.1109/icm50269.2020.9331819","title":"Optimized Random Forest Model for Botnet Detection Based on DNS Queries","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Botnet; Computer science; Random forest; Robustness (evolution); Feature selection; Domain Name System; Network packet; Classifier (UML); Data mining; The Internet; Domain name; Conditional random field; F1 score; Network security; Computer network; Machine learning; Computer security; Artificial intelligence; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002084227,0.001314181,0.001589266,0.001627979,0.0004805331,0.0007875917,0.001568985,0.001374094,0.001218321],"category_scores_gemma":[0.002734418,0.0003178241,0.001326378,0.001167715,0.0003274257,0.001119476,0.0003650107,0.001118865,0.000732264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000869068,"about_ca_system_score_gemma":0.001107547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01356026,"about_ca_topic_score_gemma":0.01501446,"domain_scores_codex":[0.9992501,0.0002245274,0.00004376813,0.000224084,0.0001157206,0.0001418555],"domain_scores_gemma":[0.9987701,0.0006834003,0.00009947375,0.00006481702,0.0003347951,0.00004748024],"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":[0.0005984524,0.0004070766,0.008951793,0.0001653278,0.0001686815,0.0001984942,0.00005614083,0.7639254,0.004330704,0.002038594,0.009445564,0.2097139],"study_design_scores_gemma":[0.00001045958,0.00002498007,0.0004675633,0.000005554434,0.00001540427,0.00001991258,0.000004889623,0.9982165,0.0003165314,0.0007148999,0.0001988225,0.000004587754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1706171,0.002619029,0.8168197,0.0008170762,0.0002010239,0.0002582853,0.001965478,0.004587627,0.002114795],"genre_scores_gemma":[0.7905232,0.0006267059,0.1989125,0.0003649789,0.0002216829,0.0003410222,0.005749659,0.0002015532,0.003058768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01356026,"threshold_uncertainty_score":0.0269627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03120826219732498,"score_gpt":0.2505682631663043,"score_spread":0.2193600009689793,"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."}}