{"id":"W3043946492","doi":"10.1145/3395351.3399442","title":"Protecting wi-fi beacons from outsider forgeries","year":2020,"lang":"en","type":"article","venue":"","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds Wetenschappelijk Onderzoek; Vlaamse regering; York University; New York University Abu Dhabi","keywords":"Beacon; Computer science; Computer network; Adversary; Computer security; Overhead (engineering); Scheme (mathematics); Bandwidth (computing); Operating system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009687419,0.0000989675,0.0001312626,0.00001107116,0.0001442347,0.0002269658,0.0004533829,0.00004599111,0.0001223947],"category_scores_gemma":[0.00004667759,0.00008036674,0.00004790601,0.0001866195,0.00001923655,0.0003607693,0.0002588021,0.0001690498,0.00009467889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006543836,"about_ca_system_score_gemma":0.00003525372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001017783,"about_ca_topic_score_gemma":0.00002327518,"domain_scores_codex":[0.9991547,0.00004520543,0.0001559403,0.0003025772,0.0001345835,0.0002069978],"domain_scores_gemma":[0.9994651,0.00009603662,0.00005055951,0.0002462234,0.0000287081,0.0001133767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006767107,0.0001112077,0.0126567,0.0001046456,0.0001600644,0.0001129461,0.01919913,0.001662901,0.008010943,0.1571111,0.08648291,0.7143198],"study_design_scores_gemma":[0.001204043,0.0004193484,0.002993497,0.0001154679,0.00001145485,0.000007520593,0.0004285888,0.6235663,0.05036045,0.03026048,0.2895634,0.001069503],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002614928,0.00003072775,0.9774218,0.01204767,0.0001034503,0.002418103,0.000002228487,0.0004173612,0.004943734],"genre_scores_gemma":[0.8616678,0.000001261081,0.1302754,0.005551378,0.0005198903,0.00167524,0.000002112807,0.00001389065,0.0002930778],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8590528,"threshold_uncertainty_score":0.327726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03915632835912281,"score_gpt":0.2299196254680748,"score_spread":0.190763297108952,"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."}}