{"id":"W3080177360","doi":"10.1109/iaict50021.2020.9172014","title":"Implementation of Ensemble Learning and Feature Selection for Performance Improvements in Anomaly-Based Intrusion Detection Systems","year":2020,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":146,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Intrusion detection system; Ensemble learning; Feature selection; Machine learning; Artificial intelligence; Anomaly detection; Decision tree; Boosting (machine learning); Data mining; Gradient boosting; Ensemble forecasting; Network security; Random forest; Computer security","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002281706,0.00008426117,0.0001149711,0.0001089451,0.0001222507,0.00005607961,0.0000769668,0.00006593688,0.000004677679],"category_scores_gemma":[0.00001335581,0.00008250991,0.0000228564,0.0004304457,0.000007897615,0.0004171118,0.00004228244,0.000118308,7.663755e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004366731,"about_ca_system_score_gemma":0.00002535136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002155201,"about_ca_topic_score_gemma":0.0002424402,"domain_scores_codex":[0.9992309,0.0000510992,0.0002074451,0.0002450103,0.0001267652,0.000138806],"domain_scores_gemma":[0.9996622,0.00003165776,0.0001358347,0.0000515422,0.000079216,0.00003959854],"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":[0.0002565386,0.00003542355,0.03151737,0.0004156021,0.00001259158,2.308242e-7,0.0008490246,0.006455089,0.3653708,0.0005978827,0.00007632166,0.5944131],"study_design_scores_gemma":[0.0006836824,0.0009156691,0.004385042,0.00001729529,0.000003242759,0.000001443338,0.0001227847,0.7765271,0.2163914,0.00001799638,0.0008600541,0.00007426311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6075953,0.00002767521,0.3917366,0.0001425941,0.0001092529,0.0003146376,3.061076e-7,0.00004928014,0.00002427647],"genre_scores_gemma":[0.9962581,0.00002415989,0.003506966,0.00008702251,0.000056148,0.00004204842,0.000003003582,0.000005402915,0.00001719003],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.770072,"threshold_uncertainty_score":0.3364656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01036222087742225,"score_gpt":0.2430767249434029,"score_spread":0.2327145040659807,"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."}}