{"id":"W4411487711","doi":"10.1016/j.vehcom.2025.100946","title":"Securing the unforeseen: Enhancing VANET security with dynamic honeypots and attack rate analysis","year":2025,"lang":"en","type":"article","venue":"Vehicular Communications","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Honeypot; Computer science; Computer security; Vehicular ad hoc network; Security analysis; Telecommunications; Wireless ad hoc network; Wireless","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":[0.000522321,0.0002400679,0.00031123,0.0002419614,0.00051961,0.0001498077,0.0008676938,0.0001057585,0.00001180807],"category_scores_gemma":[0.00003236105,0.000190278,0.0001110372,0.00169192,0.0002096495,0.0001545759,0.000397469,0.0005929771,0.00001257198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001053965,"about_ca_system_score_gemma":0.00004076933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008320465,"about_ca_topic_score_gemma":0.008492938,"domain_scores_codex":[0.9987007,0.0002317252,0.0003145,0.0002383974,0.0001560842,0.0003585789],"domain_scores_gemma":[0.9970695,0.0003481526,0.00006020662,0.002352517,0.00008555323,0.00008405961],"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":[0.000005384306,0.00002621351,0.001826132,0.00006283574,0.001527596,0.000005902428,0.000687124,0.9929564,0.0004967066,0.001303414,0.0002909811,0.0008112984],"study_design_scores_gemma":[0.000241652,0.000008731855,0.01160515,0.00008677309,0.0006864805,0.000008692668,0.0002369806,0.9767078,0.0001778161,0.0003019724,0.009711018,0.0002269432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9464377,0.01322842,0.03219381,0.002382348,0.00008089004,0.0006717071,0.00002727057,0.0005354758,0.004442425],"genre_scores_gemma":[0.9962799,0.001586401,0.001631051,0.0001252801,0.00001276344,0.00009286682,0.0001235885,0.0000316927,0.0001164763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04984222,"threshold_uncertainty_score":0.7759312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00583218368474769,"score_gpt":0.2343396969087609,"score_spread":0.2285075132240132,"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."}}