{"id":"W4386207812","doi":"10.36939/ir.202308281619","title":"Securing Intrusion Detection Systems in IoT Networks Against Adversarial Learning: A Moving Target Defense Approach based on Reinforcement Learning","year":2023,"lang":"en","type":"dissertation","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Reinforcement learning; Adversarial system; Computer science; Intrusion detection system; Artificial intelligence; Computer security; State (computer science); Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002041429,0.0006155648,0.0007541415,0.0004197445,0.0003137145,0.000853029,0.001115188,0.0007649609,0.0008899503],"category_scores_gemma":[0.005405356,0.0002435636,0.0005052817,0.0002311443,0.001067196,0.001277896,0.000982789,0.001526517,0.0001615692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001149362,"about_ca_system_score_gemma":0.001022613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002097205,"about_ca_topic_score_gemma":0.00151699,"domain_scores_codex":[0.9992205,0.0002990073,0.00003636337,0.0001466652,0.0001889665,0.0001085297],"domain_scores_gemma":[0.9967372,0.00204536,0.000402555,0.0002962014,0.0003911702,0.0001275004],"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":[0.0000510014,0.00005416013,0.001037508,0.00002730827,0.00002294271,0.00003951741,0.00003316187,0.9719416,0.001995209,0.009124853,0.0002250107,0.01544762],"study_design_scores_gemma":[0.000002266559,0.00002304498,0.00007472635,0.000002035582,0.000002128362,0.00000851875,0.000003126792,0.997588,0.0003549354,0.001868672,0.00007058666,0.00000192459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09032798,0.0002551532,0.9055121,0.0004916574,0.00003919407,0.0001090715,0.00003308031,0.0003535816,0.002878225],"genre_scores_gemma":[0.958975,0.0001478685,0.03961552,0.00009376477,0.00001779152,0.00006700862,0.00002688389,0.0000155015,0.001040658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002097205,"threshold_uncertainty_score":0.01079625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01008836647354763,"score_gpt":0.2170229045217343,"score_spread":0.2069345380481867,"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."}}