{"id":"W4408358661","doi":"10.1109/vcc63113.2024.10914473","title":"ConvLSTMTransNet: A Hybrid Deep Learning Approach for Internet Traffic Telemetry","year":2024,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University; University of Northern British Columbia","funders":"","keywords":"Telemetry; Computer science; The Internet; Internet traffic; Computer network; Deep learning; Telecommunications; Multimedia; Artificial intelligence; 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":[0.0005883494,0.001182501,0.000517357,0.000729128,0.0002570584,0.0007048945,0.001691628,0.0009134446,0.002718166],"category_scores_gemma":[0.0009448922,0.0004326276,0.0006721177,0.0006994198,0.0003370007,0.001433676,0.000828183,0.001404572,0.001055298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000948085,"about_ca_system_score_gemma":0.001191878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01506882,"about_ca_topic_score_gemma":0.0207026,"domain_scores_codex":[0.9997656,0.00003148942,0.00001202421,0.00007865297,0.00007470029,0.00003750658],"domain_scores_gemma":[0.9997312,0.00007016853,0.00003087998,0.00003490923,0.0001123356,0.00002037071],"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":[0.000211814,0.0002437837,0.002802713,0.0001181286,0.0001740845,0.0001772854,0.00004628895,0.6519639,0.01180736,0.004411072,0.01048201,0.3175615],"study_design_scores_gemma":[0.000002887732,0.00002477802,0.000138271,0.000003939097,0.00000709026,0.00001709066,0.000003727983,0.9968009,0.001512371,0.000714512,0.0007699874,0.000004527841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05821547,0.001156847,0.9258986,0.0006056243,0.0003145775,0.0000847043,0.0009725071,0.00638141,0.006370195],"genre_scores_gemma":[0.7601056,0.0008819146,0.2164682,0.0006752731,0.0001656723,0.0001883196,0.003117129,0.0003731186,0.01802479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01506882,"threshold_uncertainty_score":0.02996224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01254794343999923,"score_gpt":0.22794916370459,"score_spread":0.2154012202645908,"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."}}