{"id":"W4400508553","doi":"10.4108/eetiot.6574","title":"Mitigating Adversarial Reconnaissance in IoT Anomaly Detection Systems: A Moving Target Defense Approach based on Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"EAI Endorsed Transactions on Internet of Things","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Anomaly detection; Reinforcement learning; Computer science; Artificial intelligence; Internet of Things; Anomaly (physics); Computer security; Machine learning","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":[0.001538987,0.0005395593,0.000673153,0.0003970853,0.0002792581,0.0004819257,0.00107486,0.0007219611,0.0006181667],"category_scores_gemma":[0.004171336,0.0001976381,0.0003278117,0.0002393165,0.0009057088,0.000958748,0.0009424938,0.001124626,0.0001052293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007142002,"about_ca_system_score_gemma":0.0008293067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001876933,"about_ca_topic_score_gemma":0.001562619,"domain_scores_codex":[0.9993999,0.0002308563,0.00002389393,0.0001281437,0.0001331785,0.00008396325],"domain_scores_gemma":[0.9977651,0.001303054,0.0003171894,0.0002090034,0.0002812287,0.0001244441],"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":[0.00004023089,0.00006442349,0.000981493,0.00002092572,0.0000224402,0.00004192321,0.0000319892,0.960303,0.002558217,0.004852996,0.0003016957,0.0307806],"study_design_scores_gemma":[0.000002786892,0.00002952179,0.00007566024,0.000001439357,0.000002594324,0.00001225852,0.000003298286,0.9980916,0.0004190784,0.001255236,0.0001043329,0.000002131582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04572052,0.0001579996,0.9523716,0.0002889921,0.0000286512,0.00004188922,0.00001146969,0.0002543569,0.001124526],"genre_scores_gemma":[0.9524946,0.00008265729,0.04657365,0.0000966617,0.00002270201,0.00003300094,0.00001424343,0.00001779169,0.0006645878],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001876933,"threshold_uncertainty_score":0.008139074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01096985263195278,"score_gpt":0.2145043552474633,"score_spread":0.2035345026155105,"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."}}