{"id":"W2001546323","doi":"10.1145/2069000.2069019","title":"Outlier detection using naïve bayes in wireless ad hoc networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Wireless ad hoc network; Node (physics); Outlier; Anomaly detection; Reliability (semiconductor); Wireless network; Bayes' theorem; Scheme (mathematics); Vulnerability (computing); Computer network; Naive Bayes classifier; Mobile ad hoc network; Data mining; Wireless; Computer security; Artificial intelligence; Bayesian probability; Support vector machine; Engineering; Network packet","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006742732,0.0008317188,0.001765567,0.002231129,0.001070526,0.001907471,0.001696956,0.001674847,0.0004005349],"category_scores_gemma":[0.02693554,0.0006675724,0.0008281625,0.001680487,0.001713589,0.002694306,0.000978097,0.00120918,0.0001893265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001047558,"about_ca_system_score_gemma":0.001039241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004519578,"about_ca_topic_score_gemma":0.002707491,"domain_scores_codex":[0.9939933,0.002927548,0.0004368816,0.0008790232,0.001506398,0.0002567666],"domain_scores_gemma":[0.9792742,0.01587729,0.001855783,0.0008986478,0.001818541,0.0002754308],"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":[0.0003947507,0.0001351118,0.01698122,0.000237094,0.0003464824,0.0006318014,0.0002870501,0.7895265,0.00269643,0.02582316,0.001542253,0.1613982],"study_design_scores_gemma":[0.000009471464,0.00003736624,0.0004525489,0.00001294065,0.00002008475,0.000113977,0.0000270399,0.9812882,0.0006145706,0.01713512,0.0002728016,0.00001596875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02935401,0.0005955532,0.9689744,0.0002419264,0.00005105965,0.0000468199,0.00003116868,0.0002665916,0.0004384814],"genre_scores_gemma":[0.7908772,0.0006526373,0.2071165,0.0001577058,0.0001866484,0.0001061403,0.0001369239,0.00003376234,0.0007323759],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006742732,"threshold_uncertainty_score":0.03565937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0288807188034919,"score_gpt":0.2241019385430531,"score_spread":0.1952212197395612,"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."}}