{"id":"W4386432038","doi":"10.1109/access.2023.3311822","title":"A Collaborative DNN-Based Low-Latency IDPS for Mission-Critical Smart Factory Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Exploit; Intrusion detection system; Latency (audio); Low latency (capital markets); Anomaly detection; Deep learning; Artificial intelligence; Machine learning; Industrial control system; Automation; Identification (biology); Data mining; Computer security; Computer network; Control (management); Telecommunications","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":[0.0005805793,0.0007897298,0.0006124828,0.0004127237,0.000430911,0.0007305775,0.001780219,0.0006544982,0.001802231],"category_scores_gemma":[0.0007754783,0.0002468887,0.0004122877,0.0002922971,0.0003540147,0.001193785,0.001030967,0.001084381,0.0004591785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001449903,"about_ca_system_score_gemma":0.001227629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009430114,"about_ca_topic_score_gemma":0.01333964,"domain_scores_codex":[0.9995866,0.00004377318,0.0000275518,0.0001115903,0.0001386766,0.00009179713],"domain_scores_gemma":[0.9996647,0.00007076268,0.00003726727,0.00003158332,0.0001580132,0.00003764142],"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":[0.0006469819,0.0003225347,0.002086486,0.0001730125,0.0001108191,0.0003394946,0.00009659903,0.5977226,0.0215424,0.006685738,0.008145195,0.362128],"study_design_scores_gemma":[0.00001007627,0.00008350399,0.0001540065,0.000006459839,0.00001459345,0.00003168653,0.000008846967,0.9938671,0.004282619,0.0008113082,0.0007227071,0.000007007531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05773817,0.001109139,0.9297139,0.0004116782,0.0003350404,0.0001449177,0.0002240474,0.002627997,0.007695071],"genre_scores_gemma":[0.8810306,0.0003958101,0.111483,0.0004639307,0.00007324415,0.00009863741,0.0004165594,0.00005333832,0.005984933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009430114,"threshold_uncertainty_score":0.01875043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02997801558218181,"score_gpt":0.326106199813746,"score_spread":0.2961281842315642,"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."}}