{"id":"W2802369598","doi":"10.1007/978-3-319-73951-9_3","title":"Application of Machine Learning Techniques to Detecting Anomalies in Communication Networks: Datasets and Feature Selection Algorithms","year":2018,"lang":"en","type":"book-chapter","venue":"Advances in information security","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Hebei University; Simon Fraser University; University of Oregon","keywords":"Feature selection; Computer science; Selection (genetic algorithm); Artificial intelligence; Machine learning; Feature (linguistics); Pattern recognition (psychology); Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001985172,0.001231392,0.0008300577,0.003428972,0.0005803071,0.001863591,0.001638979,0.0009463563,0.002115157],"category_scores_gemma":[0.006345701,0.0003525768,0.001113807,0.007373684,0.0003087746,0.001738071,0.0009499251,0.001453376,0.001133412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008142316,"about_ca_system_score_gemma":0.0007527522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002805928,"about_ca_topic_score_gemma":0.003385734,"domain_scores_codex":[0.9984398,0.0003642422,0.0001349466,0.0002743033,0.0007231128,0.00006357281],"domain_scores_gemma":[0.9968721,0.001821418,0.0001606813,0.0005413604,0.000535641,0.00006887655],"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":[0.00023806,0.000478646,0.01705033,0.001117607,0.0003440336,0.0001685256,0.0001168061,0.05085295,0.005344877,0.004022469,0.09715664,0.823109],"study_design_scores_gemma":[0.00008983426,0.0005457515,0.05203192,0.000457138,0.0004287789,0.001465337,0.0003680206,0.7171461,0.03889229,0.04534,0.1430629,0.0001718564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3075177,0.04791328,0.5120027,0.006342795,0.002760464,0.0008356141,0.08905842,0.01261259,0.02095634],"genre_scores_gemma":[0.3960699,0.0174937,0.4759998,0.0005020628,0.0009102887,0.0008596629,0.09602708,0.0007120137,0.01142539],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003428972,"threshold_uncertainty_score":0.01049876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005452285389559549,"score_gpt":0.2462973031739148,"score_spread":0.2408450177843552,"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."}}