{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001705852,0.0003542511,0.0006126286,0.0002390152,0.0004872631,0.0001608934,0.001249037,0.0007218549,0.0001664482],"category_scores_gemma":[0.00007524539,0.0003591896,0.0003984125,0.000675465,0.00004473214,0.0001558235,0.003442838,0.001286327,0.00000687121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000407257,"about_ca_system_score_gemma":0.0002060202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000546543,"about_ca_topic_score_gemma":0.0002394181,"domain_scores_codex":[0.9967368,0.0004164498,0.0008712085,0.0009293318,0.0006017552,0.0004444532],"domain_scores_gemma":[0.9975314,0.000258042,0.0007483942,0.001111791,0.0002332279,0.0001171737],"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":[0.001870695,0.0007653252,0.0007412556,0.002501101,0.0007547565,0.00002452131,0.0032315,0.1430378,0.001530065,0.1602118,0.04883697,0.6364942],"study_design_scores_gemma":[0.001989372,0.001136986,0.00009297238,0.0006466247,0.0001350904,0.000046034,0.0001903526,0.8062477,0.02286198,0.04177932,0.1236377,0.001235864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09755784,0.0003121143,0.8600839,0.00027582,0.03351407,0.002828454,0.000048162,0.0009516248,0.004428049],"genre_scores_gemma":[0.9889827,0.00003645694,0.007761341,0.00008369173,0.002509254,0.000414123,0.00003725819,0.00003206746,0.0001430465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8914249,"threshold_uncertainty_score":0.999886,"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."}}