{"id":"W4417439168","doi":"10.1109/tia.2025.3645008","title":"Optimizing Data Aggregation in Power Systems to Defend Against False Data Injection Attacks","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Industry Applications","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Lakehead University","funders":"","keywords":"Data aggregator; Resilience (materials science); Vulnerability (computing); Electric power system; Phasor; Aggregate (composite); Aggregate data; Data modeling; Focus (optics)","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":[0.001524022,0.0008815826,0.0006814714,0.0004388213,0.0004155809,0.001094049,0.0005883213,0.0004848341,0.0007606224],"category_scores_gemma":[0.003594303,0.0004037062,0.0003265986,0.0006285015,0.0008672547,0.001528937,0.001142806,0.0008590897,0.0001313032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001144866,"about_ca_system_score_gemma":0.001071241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003346005,"about_ca_topic_score_gemma":0.002901909,"domain_scores_codex":[0.9990975,0.0003960493,0.00003547305,0.0001360379,0.0001774762,0.0001575561],"domain_scores_gemma":[0.998657,0.0007284917,0.0002756149,0.0001177077,0.0001507777,0.00007037521],"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":[0.00002881445,0.00001071754,0.0002826728,0.0000135973,0.00001008768,0.00001395147,0.00001932267,0.9894646,0.0008121466,0.003324042,0.0001422152,0.005877784],"study_design_scores_gemma":[0.00000349638,0.00002902727,0.0001164625,0.000002598058,0.000004784996,0.000007536158,0.000009387633,0.9958158,0.0004402374,0.003431454,0.0001369414,0.000002261609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09225924,0.0003716987,0.9033349,0.0003705527,0.00002752709,0.0000604639,0.00005388751,0.0003422894,0.003179462],"genre_scores_gemma":[0.9780012,0.0001105821,0.02123705,0.00003467194,0.000008540618,0.00002866007,0.00001854233,0.00002166997,0.0005389628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003346005,"threshold_uncertainty_score":0.008306682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04191501873805772,"score_gpt":0.3037943694542611,"score_spread":0.2618793507162034,"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."}}