{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002228302,0.00125351,0.00178098,0.003510059,0.0005356611,0.001006195,0.001231249,0.000737805,0.0009201812],"category_scores_gemma":[0.006419511,0.0004454635,0.0007608787,0.001671319,0.0003214322,0.00122038,0.0005567247,0.0009074766,0.0005723297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000495974,"about_ca_system_score_gemma":0.0006068586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001403418,"about_ca_topic_score_gemma":0.001194201,"domain_scores_codex":[0.9980354,0.0006880955,0.0002136764,0.0002332498,0.0006978051,0.0001318578],"domain_scores_gemma":[0.9970034,0.001821828,0.0002567469,0.0001568232,0.0006825272,0.00007863669],"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":[0.0004360259,0.000232815,0.004675366,0.0001890011,0.0001615873,0.0002399256,0.00006211524,0.05204003,0.01503164,0.001233591,0.003265771,0.9224321],"study_design_scores_gemma":[0.00009042476,0.0002712653,0.002701172,0.00002409985,0.00006520048,0.0002267664,0.00004392006,0.9745933,0.01753917,0.002677493,0.001735543,0.00003172307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0803601,0.0009326302,0.9135914,0.0002505723,0.00007605988,0.0002088672,0.0002017104,0.003802994,0.0005756557],"genre_scores_gemma":[0.5651283,0.0003542418,0.4323214,0.0000917802,0.0000803544,0.0003314715,0.0008472127,0.0001040296,0.0007412027],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003510059,"threshold_uncertainty_score":0.01178455,"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."}}