{"id":"W4296887680","doi":"10.18280/isi.270410","title":"Sybil Attack Detection in VANET Using Machine Learning Approach","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sybil attack; Computer science; Vehicular ad hoc network; Node (physics); Computer security; Denial-of-service attack; Scalability; Wireless ad hoc network; Computer network; Wireless sensor network; Telecommunications; Engineering; World Wide Web; Wireless; Database","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0008963416,0.0001425774,0.0001594132,0.0004895972,0.0008406472,0.0002579817,0.0003377275,0.00006658143,0.00004100191],"category_scores_gemma":[0.00006651485,0.0001608159,0.00005354125,0.001291422,0.00003496532,0.003602313,0.0003580573,0.0004934056,0.00001775003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006256442,"about_ca_system_score_gemma":0.00004849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004380063,"about_ca_topic_score_gemma":0.00004254389,"domain_scores_codex":[0.9983959,0.000262389,0.0004904543,0.0001850685,0.0003751508,0.0002910585],"domain_scores_gemma":[0.9993352,0.00003532,0.0002818912,0.0002306128,0.00006767755,0.00004927717],"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":[0.00005209806,0.0000634492,0.0009108847,0.0001182979,0.00001312337,0.000003866701,0.01034246,0.7940999,0.0006564579,0.001882995,0.00003966041,0.1918169],"study_design_scores_gemma":[0.0003225914,0.0001287319,0.000584687,0.00001535733,0.00000307791,0.0001643245,0.0003942346,0.9876246,0.0006042451,0.0007730371,0.009199012,0.000186093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4463881,0.0001134605,0.54987,0.0000149595,0.0005704572,0.0002669374,0.00000297567,0.0002604265,0.002512693],"genre_scores_gemma":[0.9938964,0.00001468054,0.00579516,0.0001126912,0.00004954344,0.00006210839,0.00003609298,0.00000782946,0.00002550686],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5475083,"threshold_uncertainty_score":0.6557882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02106561409627886,"score_gpt":0.2283816221280339,"score_spread":0.2073160080317551,"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."}}