{"id":"W2111890927","doi":"10.1109/ares.2006.7","title":"A hybrid network intrusion detection technique using random forests","year":2006,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":195,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Air Force Research Laboratory","keywords":"Intrusion detection system; Misuse detection; Anomaly detection; Anomaly-based intrusion detection system; Computer science; Anomaly (physics); Data mining; Network security; Artificial intelligence; False positive rate; Pattern recognition (psychology); Computer security","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":[0.003108122,0.001195299,0.001653948,0.003236052,0.0006498735,0.0007813691,0.001805974,0.00118738,0.0009027094],"category_scores_gemma":[0.003946739,0.0005266472,0.001651175,0.002275925,0.0003539984,0.001866468,0.000932751,0.0009440847,0.0007301279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003931541,"about_ca_system_score_gemma":0.0006110097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002894914,"about_ca_topic_score_gemma":0.004920365,"domain_scores_codex":[0.9978655,0.0005563446,0.0001265301,0.000448945,0.0008403562,0.0001623819],"domain_scores_gemma":[0.9978642,0.0009550527,0.0002434849,0.0002754594,0.0005721533,0.00008970303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003614635,0.0003520052,0.008446638,0.000205645,0.0006720216,0.0003635613,0.0001366564,0.1528765,0.02469074,0.003588618,0.008149171,0.800157],"study_design_scores_gemma":[0.00002839857,0.000139136,0.001905108,0.00001587849,0.00009897037,0.000462039,0.00001935466,0.9817361,0.008207946,0.004247948,0.003090189,0.0000489037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01589884,0.0003487321,0.9785049,0.0001266594,0.00007794552,0.0001102976,0.0001813837,0.004295998,0.0004552559],"genre_scores_gemma":[0.2365928,0.0002727798,0.7603551,0.0001676305,0.0001241116,0.0002042014,0.0006417527,0.0001872512,0.001454399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003236052,"threshold_uncertainty_score":0.01643753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008564487096383336,"score_gpt":0.2180265338015144,"score_spread":0.2094620467051311,"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."}}