{"id":"W4399562988","doi":"10.1109/ticps.2024.3413605","title":"Early Detection of Cyber-Physical Attacks on Electric Vehicles Fast Charging Stations Using Wavelets and Deep Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Cyber-Physical Systems","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet; Computer science; Artificial intelligence; Real-time computing; Automotive engineering; Computer security; Environmental science; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000153646,0.0003199087,0.0004360642,0.0003811201,0.0003393459,0.0001720762,0.0001162658,0.0002432159,0.000003618049],"category_scores_gemma":[0.00001272613,0.0003068061,0.0001864316,0.0009670236,0.00008572756,0.0003130913,0.000002278165,0.001321955,0.00003733961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002195211,"about_ca_system_score_gemma":0.00003932197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003836826,"about_ca_topic_score_gemma":0.00001936112,"domain_scores_codex":[0.9981719,0.0001648061,0.0003849045,0.0004102707,0.0004517337,0.0004163643],"domain_scores_gemma":[0.9990131,0.0005290222,0.00006394836,0.0001804415,0.00005474601,0.0001587976],"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":[0.00007090247,0.0001748264,0.00001516075,0.0001526663,0.0001949752,0.00001352445,0.002379513,0.6207335,0.3072354,0.0002656331,0.000007877926,0.06875595],"study_design_scores_gemma":[0.000532555,0.000496125,0.0001799922,0.0004814897,0.0001412209,0.00002275385,0.0003566252,0.8591489,0.1378808,0.00002386164,0.0003522226,0.0003834807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8976379,0.0001196878,0.09968251,0.000014374,0.001640921,0.000385732,0.00003046151,0.0003513594,0.0001370335],"genre_scores_gemma":[0.998646,0.00003264799,0.000009276689,0.000002510873,0.001127736,0.00005303535,0.000002669258,0.00006445748,0.00006170836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2384153,"threshold_uncertainty_score":0.9999384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02077021125439491,"score_gpt":0.2419849606241363,"score_spread":0.2212147493697414,"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."}}