{"id":"W4367400392","doi":"10.1016/j.compeleceng.2023.108732","title":"An explainable efficient flow-based Industrial IoT intrusion detection system","year":2023,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"Seneca Polytechnic; Toronto Metropolitan University","funders":"","keywords":"Intrusion detection system; Context (archaeology); Computer science; Internet of Things; Refinery; Feature (linguistics); Work (physics); Data mining; Computer security; Engineering; Environmental 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":[],"consensus_categories":[],"category_scores_codex":[0.0005044391,0.0002342223,0.0002525631,0.0006389127,0.0002982776,0.0002427038,0.0006331964,0.0002076802,0.000002792699],"category_scores_gemma":[0.00005567703,0.0002451405,0.0001005711,0.003179662,0.00001162996,0.0001917569,0.000150473,0.0004696133,0.00006473116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003755965,"about_ca_system_score_gemma":0.00005528606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001836677,"about_ca_topic_score_gemma":9.426437e-7,"domain_scores_codex":[0.9979675,0.00009818688,0.0003324815,0.0005534019,0.0004215557,0.0006268114],"domain_scores_gemma":[0.9989678,0.0001839652,0.00007007601,0.0004726319,0.00006974773,0.0002357579],"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":[0.00001700114,0.0000344691,0.000002745141,0.00001953279,0.00000695756,0.00002988741,0.00006359181,0.8641868,0.00705022,0.001093764,0.0001775068,0.1273176],"study_design_scores_gemma":[0.0005091789,0.0003581664,0.00008204143,0.0000645234,0.000005592584,0.0000228601,0.000004203062,0.9788269,0.01796334,0.00001567742,0.00187759,0.0002699024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1487986,0.00003325145,0.8459905,0.0000624607,0.00243646,0.0002239844,4.904776e-7,0.002437076,0.0000172389],"genre_scores_gemma":[0.988475,0.000002595033,0.01073593,0.0000497561,0.0006653214,0.00003538104,0.000006130652,0.00002510495,0.00000479754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8396764,"threshold_uncertainty_score":0.9996541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01091147495582619,"score_gpt":0.1975650634257508,"score_spread":0.1866535884699246,"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."}}