{"id":"W2903574267","doi":"10.1109/cybersecpods.2018.8560681","title":"A Bayesian Intrusion Detection Framework","year":2018,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Reversible-jump Markov chain Monte Carlo; Gibbs sampling; Intrusion detection system; Gaussian process; Markov chain Monte Carlo; Algorithm; Correctness; Artificial intelligence; Laplace's method; Machine learning; Gaussian; Bayesian probability","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":[0.003779905,0.00111885,0.001663283,0.002980608,0.0007754753,0.002890259,0.00358824,0.001976022,0.004768906],"category_scores_gemma":[0.008233313,0.000902862,0.001316868,0.00197607,0.001384955,0.003726955,0.002201774,0.00209068,0.001549307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002146609,"about_ca_system_score_gemma":0.002613421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008922568,"about_ca_topic_score_gemma":0.006871508,"domain_scores_codex":[0.9971613,0.001010933,0.0001261248,0.0006906382,0.0007983746,0.0002127234],"domain_scores_gemma":[0.9978182,0.001151113,0.0002014026,0.0001952599,0.0005213231,0.0001127108],"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":[0.00009268544,0.0001363008,0.003275647,0.0001701086,0.0002330471,0.0002248214,0.0002087507,0.4122254,0.001170564,0.3911146,0.007542182,0.1836059],"study_design_scores_gemma":[0.000008936888,0.00001487917,0.0002148171,0.00001586205,0.00002051774,0.00006878171,0.00001540817,0.9252204,0.0002415581,0.06968781,0.004475995,0.00001502737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002572762,0.0005045593,0.9911681,0.0005786015,0.00005345594,0.00006345743,0.0002018141,0.000542374,0.004314733],"genre_scores_gemma":[0.4289916,0.002277046,0.5453773,0.0007785211,0.0005825356,0.0005817661,0.00147316,0.0002986299,0.0196394],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008922568,"threshold_uncertainty_score":0.01999027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008855979405825713,"score_gpt":0.2358134501965531,"score_spread":0.2269574707907274,"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."}}