{"id":"W2166015814","doi":"10.1109/ccnc.2009.4784780","title":"Features Selection for Intrusion Detection Systems Based on Support Vector Machines","year":2009,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Support vector machine; Computer science; Intrusion detection system; Ranking (information retrieval); Feature selection; Data mining; Artificial intelligence; Pattern recognition (psychology); Machine learning; Classifier (UML); Selection (genetic algorithm); Rank (graph theory); Mathematics","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":[0.0003211002,0.0001638362,0.0001490363,0.0002207107,0.0003555461,0.0002378274,0.0002478933,0.0001401222,0.00003038825],"category_scores_gemma":[0.00004724028,0.0001359296,0.00009354502,0.0004735179,0.000009140412,0.0003752867,0.00001907604,0.0001636657,0.00002387456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009375132,"about_ca_system_score_gemma":0.00003119935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006642811,"about_ca_topic_score_gemma":0.00008009093,"domain_scores_codex":[0.9987959,0.00007347474,0.0002134874,0.0004015879,0.0002656665,0.0002498687],"domain_scores_gemma":[0.9993547,0.00008839767,0.0000950778,0.0002622209,0.0001226331,0.00007692529],"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":[0.0004299087,0.000278506,0.00004412319,0.00004584167,0.00001299336,0.000002047815,0.0001286308,0.01370455,0.03365391,0.04238332,0.01544768,0.8938685],"study_design_scores_gemma":[0.000442375,0.002109214,0.002141384,0.00002110744,0.000006030304,0.00002582018,0.000003226321,0.9465333,0.03453691,0.001270997,0.01271141,0.0001982093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008747019,0.00002529376,0.9837506,0.001089274,0.002125228,0.0005751116,0.000001575464,0.0006662005,0.00301963],"genre_scores_gemma":[0.9934829,0.000005299768,0.004280098,0.001074058,0.0005094077,0.00004095386,0.000006287016,0.000008205513,0.0005927655],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9847359,"threshold_uncertainty_score":0.5543047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008401492943454849,"score_gpt":0.2335754653965829,"score_spread":0.2251739724531281,"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."}}