{"id":"W4308002692","doi":"10.36227/techrxiv.21431889.v1","title":"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":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"University of New South Wales","keywords":"SCADA; Computer science; Intrusion detection system; Industrial control system; Deep learning; Convolutional neural network; Perceptron; Critical infrastructure; Artificial intelligence; Artificial neural network; Internet of Things; Real-time computing; Embedded system; Computer network; 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":[0.0004631887,0.0005942446,0.0005541658,0.001008136,0.0003157053,0.0006364171,0.0006159633,0.0005483415,0.003222991],"category_scores_gemma":[0.0009705307,0.0001870299,0.0004124762,0.0005897827,0.0002133287,0.0011195,0.0005977323,0.0007309912,0.001225117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006403973,"about_ca_system_score_gemma":0.0005711428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00178079,"about_ca_topic_score_gemma":0.002326752,"domain_scores_codex":[0.9995643,0.00004487537,0.000033441,0.0001196648,0.0001828654,0.00005467426],"domain_scores_gemma":[0.9996626,0.00006320758,0.000055009,0.00007508778,0.0001199405,0.00002409869],"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":[0.001200512,0.0005827232,0.02011541,0.0005323951,0.0002857225,0.000545487,0.0001464251,0.07156095,0.07102253,0.01087244,0.06110224,0.7620332],"study_design_scores_gemma":[0.00006286449,0.0003895125,0.01246982,0.00005599191,0.00009473552,0.0005660114,0.00005215945,0.8765091,0.07057583,0.006022403,0.03316258,0.00003907602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2570046,0.002363317,0.6497372,0.001202398,0.001006054,0.0009378159,0.007438131,0.04818581,0.03212468],"genre_scores_gemma":[0.8009636,0.0008327928,0.1748939,0.0004637237,0.000101328,0.0003982603,0.009271715,0.0002850582,0.01278959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003222991,"threshold_uncertainty_score":0.010782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02690344325050702,"score_gpt":0.2423329901094341,"score_spread":0.215429546858927,"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."}}