{"id":"W4386396915","doi":"10.32604/cmc.2023.040567","title":"Fusion of Feature Ranking Methods for an Effective Intrusion Detection System","year":2023,"lang":"en","type":"article","venue":"Computers, materials & continua/Computers, materials & continua (Print)","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Random forest; Data mining; Feature (linguistics); Artificial intelligence; Support vector machine; Machine learning; Intrusion detection system; Oversampling; Constant false alarm rate; Decision tree; Pattern recognition (psychology)","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.007222531,0.001314554,0.002686984,0.001220278,0.0009153048,0.002020716,0.002846577,0.0009504229,0.0000726672],"category_scores_gemma":[0.0002733499,0.001305081,0.0005395087,0.001507825,0.0002374322,0.002061527,0.00264454,0.000428233,0.0001601829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003913248,"about_ca_system_score_gemma":0.0001017038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000346997,"about_ca_topic_score_gemma":0.00004380216,"domain_scores_codex":[0.9892699,0.003323178,0.002448267,0.00250203,0.0008214077,0.001635202],"domain_scores_gemma":[0.992798,0.001498329,0.002104498,0.00218525,0.001015597,0.0003983801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007210515,0.0001193757,0.00001009449,0.000818508,0.0001715947,0.00002486046,0.001532137,0.0001202874,0.7734637,0.002840261,0.002158653,0.2180195],"study_design_scores_gemma":[0.003661824,0.001548012,0.002519503,0.001761121,0.0001589179,0.0001743579,0.0001411036,0.03584585,0.9370388,0.001682309,0.01398824,0.001479915],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4534446,0.0001071982,0.519356,0.0001628757,0.02197595,0.002987,0.00007718397,0.001839415,0.00004974425],"genre_scores_gemma":[0.8379776,0.00009293633,0.1571378,0.0002603264,0.003406936,0.0005761912,0.0002475871,0.0001904377,0.0001101924],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.384533,"threshold_uncertainty_score":0.9999606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01424725346003531,"score_gpt":0.2819128998463004,"score_spread":0.2676656463862651,"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."}}