{"id":"W4388894130","doi":"10.1109/clusterworkshops61457.2023.00029","title":"A Lightweight Network Traffic Prediction Method for SmartNICs","year":2023,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Energy","keywords":"Computer science; Network traffic simulation; Traffic generation model; Network traffic control; Bandwidth (computing); Traffic classification; Network performance; Distributed computing; Computer network; Network packet","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005182689,0.0007813392,0.0008796678,0.00137261,0.0004107092,0.0006957243,0.001124063,0.0006327098,0.002861674],"category_scores_gemma":[0.001966238,0.0002747086,0.0004305085,0.0009992955,0.0002604175,0.001173746,0.0005774964,0.0008259651,0.001688791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000563464,"about_ca_system_score_gemma":0.0007553772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005025482,"about_ca_topic_score_gemma":0.004881881,"domain_scores_codex":[0.999632,0.00003904794,0.00002027837,0.0001070253,0.0001516728,0.00004989517],"domain_scores_gemma":[0.9994629,0.0001044071,0.00007376889,0.0001005022,0.0002211589,0.00003735453],"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":[0.0002376764,0.0001688592,0.005358115,0.00008334644,0.00006058149,0.0001298207,0.00006244748,0.3858326,0.02428454,0.007543078,0.007755433,0.5684835],"study_design_scores_gemma":[0.000002989195,0.00001037095,0.0002730301,0.000003141907,0.000003292869,0.00001754517,0.000003446756,0.9964285,0.001518109,0.0009304472,0.0008043941,0.000004728775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01944362,0.0001052869,0.9743925,0.00006550288,0.00006851966,0.00005050802,0.0002208952,0.004691769,0.0009613885],"genre_scores_gemma":[0.5490867,0.0002224065,0.4433396,0.0001114306,0.0001385286,0.0002119866,0.001628369,0.0004290332,0.004831973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005025482,"threshold_uncertainty_score":0.009992421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01975685485603427,"score_gpt":0.267095867924407,"score_spread":0.2473390130683728,"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."}}