{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005344175,0.0005000181,0.0003916322,0.001421957,0.0001862544,0.0006852748,0.0003932397,0.0009069773,0.0008608704],"category_scores_gemma":[0.002662808,0.0001736113,0.000237037,0.001893169,0.000333911,0.0009878258,0.0001836777,0.0003432512,0.0003863303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003896479,"about_ca_system_score_gemma":0.0002365078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001901467,"about_ca_topic_score_gemma":0.001268949,"domain_scores_codex":[0.9995596,0.0001106207,0.00003019434,0.00007114009,0.000204173,0.00002436214],"domain_scores_gemma":[0.9993696,0.0002696478,0.000101757,0.00003922001,0.0002108112,0.000008943798],"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":[0.0001777128,0.0001488919,0.008414925,0.0004265835,0.0001266762,0.0002977916,0.00006335398,0.5112268,0.01904066,0.009601452,0.004090169,0.4463849],"study_design_scores_gemma":[0.000003067286,0.00003753119,0.001886422,0.0000337797,0.0000193506,0.0001065283,0.00001329013,0.9873812,0.005091184,0.003513939,0.001897101,0.00001658267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09929201,0.004475535,0.8816148,0.0005925814,0.0002924552,0.0001106827,0.0003787645,0.002257769,0.01098547],"genre_scores_gemma":[0.7861152,0.004632085,0.1996288,0.0002142424,0.0002701572,0.0001175099,0.001020426,0.0001022263,0.007899418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001901467,"threshold_uncertainty_score":0.003780782,"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."}}