{"id":"W2979563069","doi":"10.3390/app9204221","title":"AE-CGAN Model based High Performance Network Intrusion Detection System","year":2019,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoencoder; Computer science; Artificial intelligence; Intrusion detection system; Random forest; Deep learning; Machine learning; Generative adversarial network; Intrusion; Pattern recognition (psychology); Data mining","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":[0.001290873,0.0009643395,0.0008818221,0.0007608262,0.0004397691,0.0009415802,0.001497229,0.0008225093,0.001669592],"category_scores_gemma":[0.001955583,0.0002916903,0.0006861185,0.0004197723,0.0003891595,0.001860043,0.00102362,0.001264868,0.0005810482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001165538,"about_ca_system_score_gemma":0.0008831701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006115105,"about_ca_topic_score_gemma":0.005350801,"domain_scores_codex":[0.9990758,0.000144503,0.00005588663,0.0002859861,0.0003216286,0.0001161218],"domain_scores_gemma":[0.999076,0.0001900224,0.0001080938,0.0001218077,0.0004558175,0.00004822013],"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":[0.0006292125,0.0004372614,0.007527015,0.000136548,0.0002105748,0.0003101338,0.0001224843,0.7006443,0.01823321,0.007543658,0.00830654,0.2558992],"study_design_scores_gemma":[0.000004510042,0.00003751134,0.0003383616,0.000002297863,0.000009275281,0.00003520145,0.00000310827,0.9965405,0.001962666,0.000690715,0.0003691585,0.000006736781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09359371,0.0006479439,0.8876864,0.0007157142,0.0002333757,0.0001956272,0.0003413995,0.01002484,0.006561018],"genre_scores_gemma":[0.9227528,0.0002092535,0.07261097,0.0003493256,0.00005086606,0.0001348268,0.0004412891,0.00007243859,0.003378324],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006115105,"threshold_uncertainty_score":0.01215905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008783365596927568,"score_gpt":0.1915909428739549,"score_spread":0.1828075772770273,"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."}}