{"id":"W4281650425","doi":"10.1145/3543516.3453921","title":"A Look Behind the Curtain: Traffic Classification in an Increasingly Encrypted Web","year":2021,"lang":"en","type":"article","venue":"ACM SIGMETRICS Performance Evaluation Review","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Encryption; Traffic classification; Convolutional neural network; Feature engineering; Provisioning; Data mining; The Internet; Robustness (evolution); Artificial intelligence; Computer network; Machine learning; Computer security; Deep learning; 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":[0.001013289,0.001052528,0.0004718564,0.001435696,0.0005739841,0.001462892,0.00100918,0.001300962,0.0005057066],"category_scores_gemma":[0.003626565,0.0002258554,0.000492915,0.001154481,0.0007311622,0.002693567,0.0008278476,0.00197421,0.0006890252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001178307,"about_ca_system_score_gemma":0.0005686155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009940111,"about_ca_topic_score_gemma":0.009452059,"domain_scores_codex":[0.9992914,0.0001602101,0.00003856518,0.0001970998,0.0002124727,0.0001000757],"domain_scores_gemma":[0.9988484,0.0003414058,0.0001406945,0.0002425344,0.0003488215,0.00007795331],"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":[0.001087724,0.001021435,0.08721317,0.0002763626,0.0002304254,0.001260242,0.0003680713,0.3184862,0.03170248,0.007382841,0.04493394,0.5060371],"study_design_scores_gemma":[0.00001354592,0.00006260107,0.00586941,0.00002372409,0.00001796917,0.0002458319,0.0001157503,0.9763973,0.009343353,0.004949853,0.002943678,0.00001703347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8545311,0.001445018,0.1250448,0.004177249,0.0004338429,0.0001128224,0.002535139,0.005886957,0.005833105],"genre_scores_gemma":[0.9582598,0.0003847901,0.03345793,0.0006875019,0.0001764852,0.00003176714,0.004511694,0.0001525615,0.002337616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009940111,"threshold_uncertainty_score":0.01976454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07826196662841482,"score_gpt":0.3343206018691448,"score_spread":0.2560586352407299,"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."}}