{"id":"W3127329908","doi":"10.1109/tifs.2021.3056206","title":"Reinforcement Learning-Based Physical-Layer Authentication for Controller Area Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Reinforcement learning; Authentication (law); Spoofing attack; Authentication protocol; Computer network; Artificial intelligence; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001188851,0.0001703652,0.0001915777,0.00007082551,0.0002095692,0.0001059097,0.00004538941,0.0001094464,0.00002487171],"category_scores_gemma":[0.000008076026,0.0001756961,0.0001284048,0.000155407,0.00003370225,0.0003722899,9.830096e-7,0.0002723361,0.00001142832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006492301,"about_ca_system_score_gemma":0.00002441818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002768039,"about_ca_topic_score_gemma":0.00001181234,"domain_scores_codex":[0.9991676,0.00001781987,0.0002933207,0.0001091538,0.0001704299,0.0002416816],"domain_scores_gemma":[0.9993895,0.0001095016,0.00006070974,0.0001498832,0.0001944431,0.00009601634],"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":[0.00003510767,0.00002316557,0.000001431135,0.00005033704,0.00005825563,2.725329e-7,0.0005058324,0.9907276,0.00004136353,0.0007684029,0.0004419406,0.007346279],"study_design_scores_gemma":[0.001167952,0.00007955569,0.00001379236,0.00002923433,0.00006270779,0.000002875353,0.00007819354,0.9861928,0.003210871,0.0005251283,0.008450204,0.0001866952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02312432,0.00003078522,0.9754094,0.00008886049,0.0003857164,0.0003709179,0.00002263122,0.0001841104,0.000383216],"genre_scores_gemma":[0.9990351,0.00005883622,0.0003285741,0.0001945772,0.00005140882,0.0001163592,0.0001566834,0.00001690379,0.00004159211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9759107,"threshold_uncertainty_score":0.7164679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00821658973213008,"score_gpt":0.2062263887169095,"score_spread":0.1980097989847794,"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."}}