{"id":"W4310376617","doi":"10.3390/a15120453","title":"Packet-Level and Flow-Level Network Intrusion Detection Based on Reinforcement Learning and Adversarial Training","year":2022,"lang":"en","type":"article","venue":"Algorithms","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reinforcement learning; Intrusion detection system; Network packet; Artificial intelligence; Robustness (evolution); Machine learning; Convolutional neural network; Network security; Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0008346895,0.0001729657,0.0001752131,0.0001377644,0.001358995,0.0001312121,0.0001817782,0.00007090589,0.00009420704],"category_scores_gemma":[0.00004620407,0.0001829874,0.00004512199,0.0004337127,0.00004013204,0.000269495,0.0004242851,0.0005299732,0.000004208555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009674147,"about_ca_system_score_gemma":0.00004145315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000546989,"about_ca_topic_score_gemma":0.0000195497,"domain_scores_codex":[0.9983013,0.0002194975,0.0002389421,0.0004655392,0.000435606,0.0003390777],"domain_scores_gemma":[0.9993769,0.0001620608,0.0001228715,0.0001934405,0.0000335299,0.0001112028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006609959,0.00001625408,0.00002968943,0.000005412892,0.00001083436,0.00001185693,0.001071318,0.1522901,0.0001224526,0.0003954361,0.0001531152,0.8458275],"study_design_scores_gemma":[0.000894836,0.0009040676,0.0004140123,0.00002102927,0.000008886072,0.00004369282,0.0001975789,0.9786283,0.0002184019,0.0007464672,0.01770359,0.0002191082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01771625,0.0001092469,0.9787664,0.0003508563,0.00207136,0.0002756642,0.000003498092,0.0001959324,0.0005107985],"genre_scores_gemma":[0.9744019,0.00005805249,0.02417926,0.0005810452,0.0005418497,0.00004942493,0.00001259818,0.00001565392,0.0001602076],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9566857,"threshold_uncertainty_score":0.9999411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02858019655789462,"score_gpt":0.2324489749003067,"score_spread":0.2038687783424121,"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."}}