{"id":"W3001072634","doi":"10.1109/tvt.2020.2967026","title":"Reinforcement Learning Based PHY Authentication for VANETs","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Spoofing attack; Computer network; Authentication (law); Reinforcement learning; Vehicular ad hoc network; Network packet; Wireless ad hoc network; Wireless; Computer security; Artificial intelligence; Telecommunications","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":[0.001114127,0.0006729551,0.000776744,0.0003707826,0.0004448692,0.0006960449,0.0009592898,0.0007233484,0.002117161],"category_scores_gemma":[0.003413796,0.0002428021,0.0003555823,0.0002951506,0.0009182255,0.0009952812,0.001234906,0.001241498,0.0003772448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000773658,"about_ca_system_score_gemma":0.0009330444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004265862,"about_ca_topic_score_gemma":0.002656659,"domain_scores_codex":[0.9993395,0.000208122,0.00003371746,0.0001276452,0.0001568743,0.0001341316],"domain_scores_gemma":[0.9986796,0.0006710461,0.0001709661,0.0001213169,0.0002848749,0.00007217268],"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":[0.000126949,0.00004800806,0.0007548102,0.00003793729,0.00002114278,0.00007701523,0.00004717348,0.9513997,0.002028502,0.008873725,0.0006683384,0.03591673],"study_design_scores_gemma":[0.000003931274,0.0000218777,0.00005456861,0.00000209108,0.000002449724,0.000008874239,0.000004606214,0.9978563,0.0002343649,0.001668895,0.000139164,0.000002867011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0320606,0.0003254671,0.9629153,0.0002623007,0.00008421874,0.00005257483,0.00002452631,0.0005977761,0.003677262],"genre_scores_gemma":[0.9822396,0.00008484966,0.01577696,0.00006096897,0.00001559534,0.00003181677,0.00002268073,0.00001629918,0.001751329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004265862,"threshold_uncertainty_score":0.008482099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02068829875139436,"score_gpt":0.2416256342201356,"score_spread":0.2209373354687412,"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."}}