{"id":"W2097536943","doi":"10.1109/aina.2009.10","title":"Fuzzy ESVDF Approach for Intrusion Detection Systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Intrusion detection system; Computer science; Machine learning; Fuzzy logic; Artificial intelligence; Process (computing); Data mining; Selection (genetic algorithm); Set (abstract data type); Fuzzy set","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":[0.001815518,0.0006634333,0.0008560218,0.001928814,0.0004880132,0.001095375,0.001348884,0.00093799,0.001686532],"category_scores_gemma":[0.004436515,0.0002437798,0.0007380227,0.0008922406,0.00047747,0.001089665,0.0005056199,0.001028952,0.0003277686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001108285,"about_ca_system_score_gemma":0.0008007834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006304666,"about_ca_topic_score_gemma":0.003340564,"domain_scores_codex":[0.9988285,0.0002756249,0.0001005436,0.0001777892,0.0005529199,0.00006463765],"domain_scores_gemma":[0.9984918,0.0008042264,0.00007839747,0.00007891934,0.0005184569,0.00002814066],"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":[0.0001238404,0.00008932632,0.001100657,0.0001111407,0.0000721503,0.0001465325,0.00008799681,0.5625173,0.004370936,0.01675891,0.001341533,0.4132796],"study_design_scores_gemma":[0.00000533254,0.00001459352,0.00009619902,0.000004614009,0.00000473055,0.0000211695,0.0000055798,0.9950359,0.0006650544,0.003642629,0.0004992513,0.000004785034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005852422,0.0002234238,0.9927902,0.00008004483,0.00002769237,0.00002732851,0.00002438093,0.0002199112,0.0007546103],"genre_scores_gemma":[0.4521205,0.0004069441,0.5447325,0.000130543,0.00009177892,0.0001749564,0.0001887519,0.00003888604,0.002115178],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006304666,"threshold_uncertainty_score":0.01253593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01483779261006019,"score_gpt":0.2256725460030455,"score_spread":0.2108347533929853,"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."}}