{"id":"W2464919254","doi":"","title":"An Enhanced Support Vector Machine Model for Intrusion Detection","year":2008,"lang":"en","type":"article","venue":"","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Intrusion detection system; Support vector machine; Data mining; Feature selection; Artificial intelligence; Ranking (information retrieval); Machine learning; Anomaly-based intrusion detection system; Pattern recognition (psychology); Kernel (algebra); Rough set; Network security; Ranking SVM","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":[0.002154185,0.000782546,0.001288648,0.001273709,0.0002914462,0.001419223,0.001852848,0.001218884,0.00168955],"category_scores_gemma":[0.005139202,0.0003258465,0.0008338395,0.00108833,0.0004560474,0.002089052,0.0005932684,0.001373854,0.0006998075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007795681,"about_ca_system_score_gemma":0.0006357126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002419813,"about_ca_topic_score_gemma":0.001433218,"domain_scores_codex":[0.998549,0.0005385721,0.0001114724,0.000293301,0.0003893927,0.0001183282],"domain_scores_gemma":[0.9974886,0.001223785,0.0002376064,0.0001797684,0.0008115129,0.00005865923],"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":[0.000261466,0.0001918847,0.003409261,0.0001517839,0.0001503322,0.0002125141,0.0001098428,0.83164,0.002328277,0.01488687,0.002491306,0.1441664],"study_design_scores_gemma":[0.000002922412,0.00001576193,0.00007956804,0.000002429507,0.000005380632,0.00001178052,0.000001851101,0.998502,0.0001296822,0.001077757,0.0001681255,0.000002735836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04264568,0.0006984598,0.9533655,0.0004798805,0.0001278779,0.00007263231,0.0001740801,0.0008783647,0.001557486],"genre_scores_gemma":[0.8571147,0.0004428095,0.1384926,0.0001564313,0.0001493829,0.0002077243,0.0004041302,0.00003806666,0.00299421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002419813,"threshold_uncertainty_score":0.01139253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04120985825106032,"score_gpt":0.2596226735048806,"score_spread":0.2184128152538203,"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."}}