{"id":"W4402002228","doi":"10.1007/s10207-024-00903-2","title":"Adversarial robustness of deep reinforcement learning-based intrusion detection","year":2024,"lang":"en","type":"article","venue":"International Journal of Information Security","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Mitacs","keywords":"Computer science; Adversarial system; Intrusion detection system; Reinforcement learning; Artificial intelligence; Machine learning; Robustness (evolution); Deep learning; Adversarial machine learning; Overfitting; Deep neural networks; Artificial neural network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004295954,0.0009865246,0.001310058,0.0005973624,0.0003358345,0.0009153909,0.001460974,0.001107941,0.001243203],"category_scores_gemma":[0.01411771,0.0004778777,0.0005705579,0.0003136287,0.00157793,0.001589999,0.002195574,0.002200033,0.0002011541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001282203,"about_ca_system_score_gemma":0.001156711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002438422,"about_ca_topic_score_gemma":0.001312928,"domain_scores_codex":[0.9985089,0.0005272055,0.0000628301,0.000305019,0.0003696214,0.000226417],"domain_scores_gemma":[0.9926229,0.005134677,0.0006487941,0.0005445983,0.0008157981,0.0002333132],"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":[0.0001402272,0.0000391291,0.0007570448,0.00003283441,0.00004412606,0.00003331902,0.00002102559,0.9717058,0.001995957,0.006984361,0.0004421133,0.01780395],"study_design_scores_gemma":[0.000001907452,0.00001757002,0.00007365549,0.00000218963,0.000002709227,0.000006262142,0.000001194643,0.9979978,0.0003613105,0.001500044,0.00003292007,0.000002284681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08647121,0.0005095874,0.9087416,0.0005042676,0.00009290521,0.00004587983,0.00006400664,0.0006601548,0.002910387],"genre_scores_gemma":[0.9805242,0.00009437381,0.0179827,0.00009431478,0.0000257978,0.00002684497,0.00004740613,0.0000405755,0.001163699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004295954,"threshold_uncertainty_score":0.0227195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005763070019284183,"score_gpt":0.2554729891537023,"score_spread":0.2497099191344181,"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."}}