{"id":"W2807788428","doi":"10.1080/09720510.2019.1649038","title":"Detecting intrusions in control systems : A rule of thumb, its justification and illustrations","year":2020,"lang":"en","type":"preprint","venue":"Journal of Statistics and Management Systems","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Rule of thumb; Intrusion detection system; Computer science; Rule-based system; Intrusion; Control (management); Data mining; Thumb; Computer security; Artificial intelligence; Algorithm; Geology","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":[0.03584711,0.001876943,0.002916919,0.004740752,0.002395784,0.007166238,0.004734075,0.007460005,0.001246925],"category_scores_gemma":[0.1150965,0.0009613668,0.002129104,0.00261357,0.01206547,0.005154676,0.003499198,0.007580405,0.001313969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00152219,"about_ca_system_score_gemma":0.002933455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002434131,"about_ca_topic_score_gemma":0.002056577,"domain_scores_codex":[0.9664918,0.01374373,0.004573659,0.003466403,0.01100845,0.000716048],"domain_scores_gemma":[0.8921553,0.07750542,0.004464361,0.01143295,0.01347973,0.0009622148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004458216,0.0002842549,0.007327797,0.001237005,0.0003538558,0.001765187,0.0007239408,0.08548134,0.004198462,0.6860718,0.01823014,0.1938805],"study_design_scores_gemma":[0.0001108094,0.0002122168,0.0008315402,0.001204113,0.0001214121,0.001628357,0.0001804231,0.2364842,0.004688984,0.7338258,0.02055224,0.0001597932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007820812,0.002424894,0.9735267,0.007523113,0.0006913777,0.000216467,0.0001636648,0.0005635836,0.00706939],"genre_scores_gemma":[0.1578195,0.001425261,0.8361092,0.002284095,0.0007583718,0.000305434,0.0001089613,0.00008201312,0.001107138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03584711,"threshold_uncertainty_score":0.1895799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.018297647042017,"score_gpt":0.2323837641661338,"score_spread":0.2140861171241168,"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."}}