{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001017248,0.0001732873,0.0001838387,0.0001431071,0.0008108522,0.000265339,0.0009767177,0.000109726,0.00002272117],"category_scores_gemma":[0.000004469194,0.000146275,0.00004387129,0.001354159,0.0001104517,0.00070504,0.0002263205,0.0001996392,0.0003195248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000856656,"about_ca_system_score_gemma":0.00009077559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002979611,"about_ca_topic_score_gemma":0.00001424076,"domain_scores_codex":[0.9979823,0.00004640134,0.0002665911,0.00064604,0.0005964685,0.0004622179],"domain_scores_gemma":[0.9991601,0.00007100033,0.0001571765,0.0004707582,0.00005290459,0.00008803159],"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":[0.0000340372,0.00002636254,0.0002413178,0.00006347046,0.000003255414,9.042681e-7,0.0001864781,0.8418214,0.01146411,0.08683231,0.0001688063,0.05915756],"study_design_scores_gemma":[0.0002188027,0.0001521905,0.0004755039,0.00004094489,0.000003310389,0.000006813012,0.00003326162,0.9722286,0.02478703,0.001524132,0.0003226985,0.0002067212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6049446,0.00001872506,0.3869365,0.00008300076,0.001050413,0.0002414877,2.680351e-7,0.0003081545,0.006416872],"genre_scores_gemma":[0.98041,0.00001117563,0.01896146,0.0003309718,0.0001946525,0.00003165773,8.344042e-7,0.000007290902,0.00005189921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3754654,"threshold_uncertainty_score":0.6236502,"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."}}