{"id":"W4308002693","doi":"10.36227/techrxiv.21431889","title":"A Smart Network Intrusion Detection System for Cyber Security of Industrial IoT","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; Concordia University; Lakehead University","funders":"","keywords":"SCADA; Computer science; Intrusion detection system; Industrial control system; Deep learning; Convolutional neural network; Perceptron; Critical infrastructure; Artificial intelligence; Artificial neural network; Computer network; Real-time computing; Computer security; Control (management); Engineering","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":[0.000412779,0.0006448471,0.0005980147,0.0007687694,0.0003465644,0.0005516657,0.0006625421,0.0006454044,0.004914594],"category_scores_gemma":[0.0006109,0.0002198733,0.000366002,0.0004353089,0.0002004113,0.001040714,0.0005701312,0.0006671189,0.001831029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005738381,"about_ca_system_score_gemma":0.0005196696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002333879,"about_ca_topic_score_gemma":0.003023096,"domain_scores_codex":[0.9996663,0.00003252474,0.00002312292,0.00009251256,0.0001459719,0.00003959433],"domain_scores_gemma":[0.9997867,0.00003606144,0.0000242091,0.00005246753,0.00007506115,0.00002554046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001639723,0.0007810827,0.01143153,0.0003585657,0.0002254885,0.0007258673,0.0001255244,0.05115078,0.103226,0.006453195,0.08146256,0.7424197],"study_design_scores_gemma":[0.0001146115,0.0006706525,0.01262499,0.00003771098,0.00008187925,0.0005266524,0.00004596187,0.872313,0.06898914,0.00347558,0.04107071,0.00004915617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2884776,0.001707304,0.5675005,0.001441705,0.001371287,0.001398914,0.008540674,0.0930951,0.03646693],"genre_scores_gemma":[0.7125543,0.0006656628,0.2439457,0.0007443031,0.0001168111,0.0005039054,0.01302721,0.0005275955,0.02791448],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004914594,"threshold_uncertainty_score":0.01644093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02683766170527258,"score_gpt":0.2424064316965705,"score_spread":0.2155687699912979,"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."}}