{"id":"W3167091510","doi":"10.1109/syscon48628.2021.9447072","title":"Efficient Traffic Classification Using Hybrid Deep Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Traffic classification; Computer science; Artificial intelligence; Machine learning; Convolutional neural network; Deep learning; Binary classification; Multiclass classification; Artificial neural network; Recurrent neural network; Data mining; Quality of service; Support vector machine; Computer network","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":[0.0008062057,0.0009647898,0.0008276094,0.00104353,0.0002903867,0.0008546998,0.00146366,0.0008776251,0.00111415],"category_scores_gemma":[0.00127791,0.0003967892,0.0006924206,0.0008799889,0.0003228096,0.001662221,0.000846782,0.0008617214,0.0005197547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001072985,"about_ca_system_score_gemma":0.0008393431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008129814,"about_ca_topic_score_gemma":0.01025772,"domain_scores_codex":[0.9996234,0.00005911601,0.00002527969,0.00009990123,0.0001004304,0.00009185682],"domain_scores_gemma":[0.9995092,0.0001434987,0.00005791681,0.00007524211,0.00018418,0.00002996254],"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":[0.0002164579,0.000302331,0.003326332,0.00006740219,0.0001466962,0.00008667645,0.00004437212,0.6061632,0.01162236,0.002820431,0.002439819,0.3727638],"study_design_scores_gemma":[0.00000144159,0.00001130929,0.00009507721,0.00000144529,0.00000345302,0.000004512709,0.000002440575,0.9985097,0.0008878323,0.0004017996,0.00007876527,0.00000220695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1476081,0.0006176155,0.8442501,0.0003080962,0.0000967153,0.00006006266,0.0002274003,0.003950936,0.002881011],"genre_scores_gemma":[0.9072024,0.0001987625,0.08864027,0.0001717335,0.00003944991,0.00007403733,0.000542186,0.00006037682,0.003070786],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008129814,"threshold_uncertainty_score":0.01616496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02357637689426253,"score_gpt":0.2507384555261722,"score_spread":0.2271620786319097,"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."}}