{"id":"W4410832690","doi":"10.1016/j.automatica.2025.112387","title":"False Noise Attack Detection for differentially-private distributed control of microgrids","year":2025,"lang":"en","type":"article","venue":"Automatica","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Science, Technology and Innovation Commission of Shenzhen Municipality; Clinical Special Fund of Jiangsu Province; Shenzhen Science and Technology Innovation Program; Compute Canada","keywords":"Noise (video); Control (management); Computer science; Control theory (sociology); Computer security; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00006842834,0.0001042666,0.0002049308,0.00006427459,0.00005234994,0.00001825469,0.0001346627,0.00007945363,0.00001154774],"category_scores_gemma":[0.0000798771,0.00009578976,0.00008733608,0.0001607775,0.00004669156,0.00004571186,0.00001808342,0.0000670618,0.0000079552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002718152,"about_ca_system_score_gemma":0.00001050839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003177603,"about_ca_topic_score_gemma":0.00001205667,"domain_scores_codex":[0.9993793,0.00001540222,0.0002541242,0.0001024943,0.00007151644,0.0001771942],"domain_scores_gemma":[0.9995645,0.0001478495,0.00003103444,0.0001857382,0.0000380267,0.00003288253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001414452,0.0002934343,0.001742528,0.002969572,0.0006225165,0.000003411551,0.0003976448,0.02135978,0.9249219,0.003272572,0.003668982,0.04060616],"study_design_scores_gemma":[0.001325361,0.00005535474,0.02240941,0.0001322249,0.0001095918,0.000001472388,0.00002314609,0.7078988,0.2610834,0.0007495112,0.006040235,0.0001714891],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6025712,0.0001646612,0.3962156,0.0000590464,0.0003984059,0.0002563393,0.00006586668,0.0002056658,0.00006321274],"genre_scores_gemma":[0.9992834,0.00002108004,0.0005444467,0.00002114541,0.00003301643,0.00005133529,0.00001563296,0.000009620795,0.00002029638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.686539,"threshold_uncertainty_score":0.3906193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005575079578411376,"score_gpt":0.2264718661736664,"score_spread":0.220896786595255,"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."}}