{"id":"W32779056","doi":"10.1007/s10439-020-02585-y","title":"Next Generation Intrusion Detection: Autonomous Reinforcement Learning of Network Attacks","year":2000,"lang":"en","type":"article","venue":"Annals of Biomedical Engineering","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institute of General Medical Sciences; National Institutes of Health; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Intrusion detection system; Computer science; Reinforcement learning; Artificial neural network; Retraining; Anomaly-based intrusion detection system; Artificial intelligence; Machine learning; Network security; Host-based intrusion detection system; Computer security; Intrusion prevention system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.002284706,0.0005940299,0.000825096,0.0005936872,0.0003426421,0.0006542291,0.001181546,0.0008109261,0.0008520887],"category_scores_gemma":[0.006918743,0.0002793555,0.000299525,0.0002436281,0.0007201497,0.001006984,0.000879807,0.0009766993,0.0001689508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000865289,"about_ca_system_score_gemma":0.0008956899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004762049,"about_ca_topic_score_gemma":0.003038877,"domain_scores_codex":[0.9993725,0.0002431424,0.00002857439,0.0001253293,0.0001209253,0.000109475],"domain_scores_gemma":[0.9965323,0.002208584,0.0004072908,0.0001941045,0.0004683621,0.0001893345],"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":[0.0001806829,0.000136364,0.004246072,0.00003020425,0.00006147545,0.00006524181,0.00005189129,0.9209306,0.001350302,0.003103201,0.0008800155,0.06896401],"study_design_scores_gemma":[0.000003884349,0.00001231779,0.00009727186,0.000001083424,0.000002151123,0.000005888063,0.000002280253,0.9989116,0.0001598684,0.0007494376,0.00005274748,0.000001559329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1859122,0.0004132179,0.8081235,0.0008569144,0.000096235,0.00009679287,0.00006928732,0.001447852,0.00298407],"genre_scores_gemma":[0.9722181,0.0000766421,0.02662108,0.0001001222,0.00002821665,0.00006191024,0.00004968474,0.00002529575,0.0008189387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004762049,"threshold_uncertainty_score":0.01208282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02849674137112709,"score_gpt":0.2484525173258489,"score_spread":0.2199557759547218,"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."}}