{"id":"W2808669017","doi":"10.1109/tdsc.2018.2846258","title":"Physical Layer based Message Authentication with Secure Channel Codes","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; China Postdoctoral Science Foundation","keywords":"Computer science; Authentication (law); Message authentication code; Authentication protocol; Computer network; Physical layer; Data Authentication Algorithm; PHY; Hash function; Channel (broadcasting); Theoretical computer science; Computer security; Cryptography; Wireless","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":[],"consensus_categories":[],"category_scores_codex":[0.0001392589,0.0002231114,0.000219519,0.000143337,0.0003439024,0.00008426919,0.0001983831,0.0001059234,0.00002293304],"category_scores_gemma":[0.000001928676,0.0002090632,0.00005100808,0.0002782469,0.00009289973,0.0001685346,0.000003220535,0.0003788253,0.00001808646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004145633,"about_ca_system_score_gemma":0.00002017099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000183614,"about_ca_topic_score_gemma":0.00009942509,"domain_scores_codex":[0.9990525,0.0000558672,0.0001828131,0.0002526983,0.0001952901,0.0002608865],"domain_scores_gemma":[0.9992543,0.0001124362,0.00004435655,0.0003936426,0.0001072428,0.00008805745],"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":[0.0006364643,0.002826007,0.0002072745,0.001787881,0.001375222,0.00007139236,0.06159559,0.5922557,0.1616948,0.007122875,0.003047792,0.167379],"study_design_scores_gemma":[0.0003335599,0.0001370449,0.00003910861,0.0001378134,0.00003898256,0.00001314098,0.000183332,0.7940019,0.2042344,0.0001671607,0.0004611744,0.0002524113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3060029,0.00005513544,0.6923467,0.0001170872,0.00008181524,0.0001728922,0.00001572176,0.0006903629,0.0005173525],"genre_scores_gemma":[0.9950835,0.0000221617,0.004625995,0.00008036772,0.00008345837,0.00002281008,0.000007926506,0.00005144646,0.00002240133],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6890805,"threshold_uncertainty_score":0.852535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01334786109977719,"score_gpt":0.2423302788624251,"score_spread":0.2289824177626479,"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."}}