{"id":"W4309091762","doi":"10.3390/math10214097","title":"Detection and Mitigation of GNSS Spoofing Attacks in Maritime Environments Using a Genetic Algorithm","year":2022,"lang":"en","type":"article","venue":"Mathematics","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"Korea Institute of Energy Technology Evaluation and Planning","keywords":"GNSS applications; Spoofing attack; Computer science; Real-time computing; Satellite system; Mean squared error; GNSS augmentation; Genetic algorithm; MATLAB; Algorithm; Global Positioning System; Data mining; Computer security; Telecommunications; Machine learning; Statistics; Mathematics","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":[0.0005044725,0.000666005,0.0006326776,0.0009739706,0.0003858548,0.0005999826,0.0006950066,0.0008246801,0.0004648002],"category_scores_gemma":[0.001673485,0.0002078712,0.0005806371,0.0005321664,0.0003305026,0.0004083558,0.0003652676,0.0004269587,0.0001190783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005288213,"about_ca_system_score_gemma":0.0008981075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007181476,"about_ca_topic_score_gemma":0.004011976,"domain_scores_codex":[0.9997212,0.00005753356,0.00001455745,0.00007475678,0.00008021134,0.00005177056],"domain_scores_gemma":[0.9995616,0.0001814185,0.0000736738,0.00002481224,0.0001384306,0.00002010372],"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":[0.000148813,0.0001446657,0.005063234,0.00004915328,0.00008913369,0.0001315316,0.0001004794,0.8479928,0.01118964,0.001339462,0.0004676663,0.1332833],"study_design_scores_gemma":[0.00001079964,0.00009561385,0.001030027,0.000006302287,0.00001919926,0.00004936845,0.00002486738,0.9956198,0.002498847,0.0003585627,0.0002788574,0.000007678499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2964691,0.0005450099,0.6973543,0.0003243226,0.00008184369,0.0001060225,0.00006663377,0.001074252,0.003978525],"genre_scores_gemma":[0.8272396,0.0002225497,0.1700537,0.00008590886,0.00001646629,0.00009338695,0.0001235661,0.0000305954,0.00213421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007181476,"threshold_uncertainty_score":0.01427931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00875031906668659,"score_gpt":0.2074323502647753,"score_spread":0.1986820311980887,"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."}}