{"id":"W2979434725","doi":"10.1109/iccsp.2019.8698029","title":"Artificial Intelligence based Network Intrusion Detection with Hyper-Parameter Optimization Tuning on the Realistic Cyber Dataset CSE-CIC-IDS2018 using Cloud Computing","year":2019,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":147,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Intrusion detection system; Computer science; Artificial neural network; Botnet; Cloud computing; Artificial intelligence; Machine learning; Computational intelligence; Big data; Data mining; Computer security; The Internet; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00170025,0.002182687,0.001045671,0.003269986,0.0007524714,0.001128414,0.001795286,0.001371439,0.001084743],"category_scores_gemma":[0.003892528,0.0003221894,0.001331718,0.002441104,0.0005300781,0.001207378,0.0008712586,0.001442306,0.0007506405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002085865,"about_ca_system_score_gemma":0.001318351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02353545,"about_ca_topic_score_gemma":0.02920404,"domain_scores_codex":[0.998621,0.0002705593,0.0001444215,0.0004607454,0.0002883032,0.0002149351],"domain_scores_gemma":[0.9986706,0.0003943679,0.0001543629,0.0002928358,0.0003879192,0.00009993563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002006563,0.003150015,0.06941112,0.001216237,0.001106512,0.001049678,0.0002603188,0.4477156,0.009643966,0.00341097,0.2112501,0.2497789],"study_design_scores_gemma":[0.0001228397,0.0002469853,0.01385525,0.00005046942,0.00006820182,0.0002878996,0.0001293833,0.9610903,0.008230241,0.001490874,0.01438138,0.00004616945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8178487,0.00354569,0.04476962,0.001813707,0.001293856,0.001060918,0.09629079,0.02310094,0.01027579],"genre_scores_gemma":[0.676208,0.0007195965,0.09612138,0.0005110156,0.0001609214,0.0006453089,0.2214661,0.0004020275,0.003765606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02353545,"threshold_uncertainty_score":0.04679692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03570598258581827,"score_gpt":0.2527423743127317,"score_spread":0.2170363917269134,"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."}}