{"id":"W4220789286","doi":"10.18280/ijsse.120111","title":"Phishing and Sybil Enhanced Behavior Processing and Footprint Algorithms in Vehicular Ad Hoc Network","year":2022,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sybil attack; Phishing; Computer science; Node (physics); Vehicular ad hoc network; Wireless ad hoc network; Computer network; Computer security; Mobile ad hoc network; Network packet; Wireless; Wireless sensor network; Engineering; Telecommunications; The Internet; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001178801,0.0005390625,0.0006944481,0.0008630707,0.0003350424,0.0007680023,0.000975015,0.0006761421,0.0003321141],"category_scores_gemma":[0.004410783,0.0002434535,0.0004353062,0.0005359222,0.0007982571,0.001721032,0.001041535,0.001109174,0.0001147665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007943976,"about_ca_system_score_gemma":0.0006406591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001888153,"about_ca_topic_score_gemma":0.001051996,"domain_scores_codex":[0.9990361,0.0003567518,0.00005611078,0.0001377036,0.00029175,0.0001216051],"domain_scores_gemma":[0.9980711,0.001003694,0.0002288445,0.0002607999,0.0003421924,0.00009336169],"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":[0.0004709236,0.0002475473,0.005484012,0.00009770357,0.000155752,0.0001621467,0.0003628671,0.6734393,0.0124426,0.02473407,0.002170163,0.280233],"study_design_scores_gemma":[0.000009592243,0.0001200268,0.0004637971,0.000004045136,0.00001072188,0.00006821373,0.00003120694,0.9925684,0.002259095,0.004061356,0.0003941724,0.000009476847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1377281,0.001052795,0.8571566,0.0004199801,0.00009915973,0.0000985447,0.0000271068,0.000778611,0.002639016],"genre_scores_gemma":[0.9027672,0.0004684863,0.0948272,0.00009503873,0.00006814171,0.00007196841,0.00007178087,0.00002704357,0.001603161],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001888153,"threshold_uncertainty_score":0.006234169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0070654505880687,"score_gpt":0.2250088825531402,"score_spread":0.2179434319650715,"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."}}