{"id":"W4410226682","doi":"10.1109/jiot.2025.3568503","title":"Modeling Realistic Adversarial Traffic Against Deep-Learning-Based Intrusion Detection System in Industrial IoT","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Adversarial system; Computer science; Intrusion detection system; Deep learning; Artificial intelligence; Data modeling; Computer security; 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":[0.0008610045,0.0006062164,0.0003610949,0.0003626611,0.0002455331,0.00043611,0.0007408889,0.000718371,0.0004181353],"category_scores_gemma":[0.002976222,0.0002341148,0.0003841468,0.000221061,0.0007766222,0.0008424055,0.0007670073,0.0009254421,0.0001022393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00108077,"about_ca_system_score_gemma":0.0004883477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00355948,"about_ca_topic_score_gemma":0.002436189,"domain_scores_codex":[0.9996088,0.0001245632,0.00001589423,0.00007846018,0.000086725,0.0000855637],"domain_scores_gemma":[0.9986125,0.0009000215,0.0001492165,0.0001189668,0.0001641553,0.00005517333],"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":[0.00005283238,0.00002507097,0.001236628,0.00001108236,0.000008167127,0.00003767469,0.00001737477,0.9889302,0.001145789,0.00143408,0.0002303215,0.006870813],"study_design_scores_gemma":[8.519085e-7,0.000007130399,0.00008323463,7.364937e-7,9.589799e-7,0.000004760717,0.000001691104,0.9991207,0.000368311,0.0003803244,0.00003045338,9.829336e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6289099,0.0003536235,0.3657949,0.0006215789,0.00007833307,0.00007930779,0.0001380162,0.000973866,0.003050561],"genre_scores_gemma":[0.9900979,0.00006324997,0.009136103,0.00006055498,0.000008139751,0.00002064969,0.00007189045,0.00001859402,0.0005229965],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00355948,"threshold_uncertainty_score":0.007841527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01420064349498289,"score_gpt":0.2490040581999059,"score_spread":0.234803414704923,"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."}}