{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003293673,0.0001088695,0.0001242916,0.0001088958,0.0002524382,0.0002082663,0.000333056,0.0001071012,0.000003226847],"category_scores_gemma":[0.00001951838,0.00009258452,0.0000686345,0.0003629715,0.00001050439,0.0005032699,0.00004900644,0.0001018787,0.00001739099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000464793,"about_ca_system_score_gemma":0.0000122745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002455467,"about_ca_topic_score_gemma":0.000003844956,"domain_scores_codex":[0.9990097,0.0000442793,0.0002034177,0.0003377049,0.0001795909,0.0002252629],"domain_scores_gemma":[0.999414,0.00003251844,0.00006869539,0.0003302244,0.00008490958,0.00006959796],"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":[0.00004480797,0.0001363366,0.000003543763,0.0000275849,0.00000856821,7.710854e-7,0.0002050056,0.003097611,0.009464727,0.2564078,0.002870016,0.7277332],"study_design_scores_gemma":[0.0003390698,0.0005013455,0.0001170138,0.000009024163,0.000004363513,0.00003576691,0.00002801563,0.9607986,0.00920608,0.01332902,0.01544002,0.0001917177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003768458,0.0001086081,0.9770102,0.0001966228,0.000879131,0.0004186516,3.4858e-7,0.0004029428,0.01721506],"genre_scores_gemma":[0.9637702,0.00001911228,0.03501175,0.0003194875,0.0003748136,0.00003634066,0.000002570173,0.000004633463,0.0004610988],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9600017,"threshold_uncertainty_score":0.3775487,"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."}}