{"id":"W7018179623","doi":"","title":"A Comparative Evaluation of Machine Learning Models and EDA through Tableau Using CICIDS2017 Dataset","year":2023,"lang":"en","type":"other","venue":"NORMA","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Random forest; Linear discriminant analysis; Python (programming language); AdaBoost; Decision tree; Artificial neural network; Statistical classification; Supervised learning","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":[0.0005387187,0.0001518415,0.0002784597,0.000135137,0.0001098527,0.00005304969,0.0002593342,0.0001326989,0.000136598],"category_scores_gemma":[0.00001869403,0.0001462751,0.0000263128,0.0002698219,0.00004825046,0.0004809219,0.0002491706,0.0002363101,0.00002185707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003851445,"about_ca_system_score_gemma":0.00005872738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002840302,"about_ca_topic_score_gemma":0.00121605,"domain_scores_codex":[0.9987255,0.0001903024,0.0001870531,0.0003010235,0.0004495943,0.0001465235],"domain_scores_gemma":[0.9992314,0.00004643613,0.0002954891,0.000324585,0.00007268573,0.00002940145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006971748,0.0001970703,0.0001019241,0.0003974766,0.0004955881,0.00001405045,0.01062512,0.3152748,0.0005491701,0.01530388,0.5732823,0.08368894],"study_design_scores_gemma":[0.0002559392,0.000048271,0.000006269853,0.0001567359,0.00003992549,0.000007054075,0.0000192493,0.9453849,0.0001228712,0.002131722,0.05168574,0.0001413781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004344363,0.01172144,0.7731863,0.0001276931,0.003003982,0.001905059,0.001299104,0.001247302,0.2031648],"genre_scores_gemma":[0.7244222,0.01370392,0.1561292,0.0009998776,0.004401424,0.0002681446,0.006433244,0.002364623,0.09127743],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7200778,"threshold_uncertainty_score":0.5964927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1688295358803433,"score_gpt":0.3576173925098603,"score_spread":0.188787856629517,"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."}}