{"id":"W1588841734","doi":"10.1109/ccece.2001.933764","title":"Traffic identification using artificial neural network [Internet traffic]","year":2002,"lang":"en","type":"article","venue":"","topic":"Network Packet Processing and Optimization","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Telnet; Computer science; Backpropagation; Artificial neural network; Classifier (UML); Artificial intelligence; Perceptron; The Internet; Telephony; File Transfer Protocol; Multilayer perceptron; Machine learning; Time delay neural network; Feed forward; Feedforward neural network; Data mining; Computer network; Engineering; World Wide Web","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":[0.0002286705,0.0001449405,0.0001310436,0.00007313935,0.0001998841,0.0005926874,0.000488951,0.00007412207,0.0001056442],"category_scores_gemma":[0.00001295445,0.0001394499,0.00006248938,0.0006906737,0.00003821393,0.0006961229,0.00005688181,0.0001156598,0.0001114949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003652351,"about_ca_system_score_gemma":0.00001227719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003043484,"about_ca_topic_score_gemma":0.00001177063,"domain_scores_codex":[0.998578,0.0000722597,0.0003501647,0.0004145813,0.0002301894,0.0003548659],"domain_scores_gemma":[0.9993389,0.000035123,0.0001331494,0.0003366053,0.00007389821,0.000082325],"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":[0.000002054911,0.00004526664,0.000008681604,0.000003615135,0.000004593645,0.000002807971,0.0002591568,0.847759,0.00003542779,0.003272991,0.003646765,0.1449596],"study_design_scores_gemma":[0.00009109553,0.00002255459,0.00003378538,0.00001306675,0.000007966453,0.00002019922,0.0000102929,0.9988877,0.0001269486,0.0002728539,0.0003428201,0.0001707642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1631747,0.0002541162,0.8334476,0.0004559341,0.001051055,0.0001087206,2.390751e-7,0.0005267668,0.0009809053],"genre_scores_gemma":[0.9596587,0.000009254834,0.03848474,0.0002128501,0.0004624975,0.000003991037,0.000004219293,0.00001327372,0.001150429],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7964841,"threshold_uncertainty_score":0.5715298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04402837148749212,"score_gpt":0.2499711759541549,"score_spread":0.2059428044666628,"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."}}