{"id":"W2951944995","doi":"10.1109/access.2019.2922692","title":"A Simple Recurrent Unit Model Based Intrusion Detection System With DCGAN","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Department of Science and Technology of Sichuan Province; National Natural Science Foundation of China","keywords":"Computer science; Intrusion detection system; Benchmark (surveying); Network packet; Artificial intelligence; Deep learning; Process (computing); Dependency (UML); Data mining; Moment (physics); Machine learning; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002381347,0.0001659465,0.0001731651,0.0001800753,0.0002001778,0.0003443469,0.0008249651,0.00009567111,0.00002182221],"category_scores_gemma":[0.000005412176,0.0001364291,0.00004907664,0.0007504561,0.00001826944,0.001221257,0.00014573,0.0002246599,0.0000955386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001031286,"about_ca_system_score_gemma":0.00007300211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001235484,"about_ca_topic_score_gemma":0.0002449847,"domain_scores_codex":[0.9985997,0.00008894269,0.000217403,0.0004515187,0.0003728641,0.0002695716],"domain_scores_gemma":[0.9989401,0.0000459229,0.000147757,0.000624366,0.0001451544,0.00009674809],"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":[0.000686457,0.0003072184,0.002181659,0.0005801287,0.00005368154,0.00002812915,0.0006304486,0.6585389,0.01995734,0.008069346,0.0009185327,0.3080481],"study_design_scores_gemma":[0.0004747759,0.0002515065,0.0002688207,0.00009560771,0.000006284663,0.00001205226,0.00001166243,0.9611163,0.03628666,0.0003583471,0.0009171209,0.0002008201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3932114,0.00001216482,0.6049299,0.00004116014,0.0007280475,0.0002437026,0.000001196323,0.0002489035,0.0005835735],"genre_scores_gemma":[0.9984667,0.00000430642,0.001136954,0.000186021,0.0001127316,0.000032149,0.000002697725,0.00001422101,0.00004425383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6052553,"threshold_uncertainty_score":0.5563417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02204674015156313,"score_gpt":0.2594317097932778,"score_spread":0.2373849696417147,"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."}}