{"id":"W2027176799","doi":"10.1145/2387218.2387231","title":"Preventing a DoS threat in vehicular ad-hoc networks using adaptive group beaconing","year":2012,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer network; Computer science; Denial-of-service attack; Vehicular ad hoc network; Wireless ad hoc network; Quality of service; Bandwidth (computing); Provisioning; Computer security; Wireless; The Internet; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006349027,0.0003484508,0.0003742574,0.0001407069,0.00009260674,0.00005512374,0.0001867925,0.0002434278,0.0001048407],"category_scores_gemma":[0.00001432561,0.0003579453,0.0001358462,0.0004492078,0.00003152858,0.0006339518,0.0001170892,0.0005756154,0.00002903167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002941406,"about_ca_system_score_gemma":0.00001086698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003565918,"about_ca_topic_score_gemma":0.0001410648,"domain_scores_codex":[0.9977874,0.00008747175,0.0004221906,0.0002786908,0.0002246612,0.001199598],"domain_scores_gemma":[0.9992971,0.00009116338,0.00005721582,0.0003210698,0.00002308719,0.0002103217],"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":[0.000009387632,0.00003398299,0.006792356,0.00002350637,0.00007694688,0.00002395478,0.0003106413,0.9840585,0.000910193,0.0002809991,0.00006823409,0.007411292],"study_design_scores_gemma":[0.0004641067,0.00002185299,0.00373259,0.0002283757,0.00004630634,0.00004232665,0.0001824966,0.9936238,0.0002005131,0.0000448382,0.0009785919,0.0004341983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7775825,0.02280691,0.1955576,0.000007482807,0.0005867539,0.0004863116,0.000001025485,0.0005248067,0.002446571],"genre_scores_gemma":[0.9790803,0.000187223,0.02000049,0.00005068947,0.0004554641,0.00003506466,0.0000130687,0.0001171792,0.00006055176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2014977,"threshold_uncertainty_score":0.9998872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01584315941726305,"score_gpt":0.2213705670476242,"score_spread":0.2055274076303611,"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."}}