{"id":"W2752671358","doi":"10.1109/dsn.2017.10","title":"Voiceprint: A Novel Sybil Attack Detection Method Based on RSSI for VANETs","year":2017,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Sybil attack; Computer science; Wireless ad hoc network; Vehicular ad hoc network; Computer security; Computer network; Real-time computing; Data mining; Wireless sensor network; Telecommunications; 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":[0.0004269921,0.00022572,0.0002240509,0.00007501516,0.0002437351,0.00014306,0.0003106942,0.0001686769,0.00008951329],"category_scores_gemma":[0.0001296837,0.0002158466,0.000140694,0.00004924372,0.00002086321,0.0001492388,0.00003756631,0.0002125966,0.0001128557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001114457,"about_ca_system_score_gemma":0.00001501051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000409996,"about_ca_topic_score_gemma":0.0003320938,"domain_scores_codex":[0.998894,0.00001914866,0.0002002412,0.0003139125,0.0001737925,0.0003989138],"domain_scores_gemma":[0.9987116,0.0001864858,0.00006108864,0.0008663636,0.00005130268,0.0001231489],"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":[0.00002573769,0.00002745448,0.00002739735,0.00005161906,0.00003467327,0.000002278315,0.00001437586,0.9633695,0.007690778,0.0001203964,0.000952685,0.02768306],"study_design_scores_gemma":[0.0009422044,0.00006901851,0.001627001,0.00003614804,0.00002873508,0.000006107017,0.000005092052,0.9405077,0.02127278,0.00005276898,0.03519126,0.0002612588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01990018,0.00001647443,0.9658895,0.0001766045,0.0007307067,0.0005199507,0.00001043611,0.0004849897,0.01227112],"genre_scores_gemma":[0.9160272,0.000003239697,0.08262961,0.0001904381,0.0003103002,0.0001144787,0.000009302981,0.00007903048,0.0006363399],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8961271,"threshold_uncertainty_score":0.8801968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0310301049686057,"score_gpt":0.2985441763315375,"score_spread":0.2675140713629318,"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."}}