{"id":"W1729220625","doi":"10.1109/ccece.2000.849552","title":"Traffic identification using Bayes' classifier","year":2002,"lang":"en","type":"article","venue":"","topic":"Network Packet Processing and Optimization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Naive Bayes classifier; Byte; Classifier (UML); Traffic classification; Bayes' theorem; Data mining; Quality of service; Bayes classifier; The Internet; Network packet; Server; Bayes error rate; Artificial intelligence; Machine learning; Computer network; Bayesian probability; World Wide Web; Support vector machine; Operating system","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":[0.004446604,0.001146606,0.002000352,0.004376985,0.001011716,0.002474855,0.001260425,0.0023179,0.002152515],"category_scores_gemma":[0.0229158,0.0006525777,0.000989091,0.001769181,0.0006779944,0.003044603,0.000630601,0.001431127,0.001584257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000852975,"about_ca_system_score_gemma":0.001172606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004287642,"about_ca_topic_score_gemma":0.00196933,"domain_scores_codex":[0.9967883,0.0008637896,0.000339336,0.0005852175,0.001181736,0.0002416553],"domain_scores_gemma":[0.9899603,0.007422372,0.0004835061,0.0004179231,0.001606171,0.0001097675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001022629,0.0002898925,0.02482415,0.0003096233,0.0002846232,0.0004663853,0.0002961948,0.1493597,0.008591615,0.02292535,0.009403221,0.7822266],"study_design_scores_gemma":[0.00003330855,0.00006725208,0.001740408,0.00004239942,0.00005877439,0.0002802819,0.00004242939,0.9741537,0.004251068,0.01718165,0.002111312,0.00003739617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03839713,0.000652969,0.9563218,0.0002450283,0.0001888538,0.0001482097,0.0001994781,0.001462107,0.002384393],"genre_scores_gemma":[0.5404795,0.0009896145,0.4520186,0.0002609905,0.0004734535,0.0003435246,0.0008520814,0.0001273496,0.004454873],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004446604,"threshold_uncertainty_score":0.02351618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04397368960835139,"score_gpt":0.2437218741185943,"score_spread":0.1997481845102428,"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."}}