{"id":"W2127673234","doi":"10.1109/cec.2011.5949799","title":"Is machine learning losing the battle to produce transportable signatures against VoIP traffic?","year":2011,"lang":"en","type":"article","venue":"","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"National Institute for Materials Science; Mitacs; Dalhousie University","keywords":"Computer science; AdaBoost; Sampling (signal processing); Voice over IP; Network packet; Naive Bayes classifier; Random forest; Artificial intelligence; Traffic classification; Machine learning; Data mining; Deep packet inspection; Encryption; Computer network; The Internet; Support vector machine; Operating system","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":[0.009427527,0.0007303304,0.001469469,0.0009727409,0.0008229222,0.002021209,0.001757798,0.003004804,0.001534766],"category_scores_gemma":[0.03756237,0.0004067551,0.0006941748,0.001149185,0.001979002,0.004771836,0.0008118392,0.002859862,0.001457201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001064939,"about_ca_system_score_gemma":0.001484429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003149308,"about_ca_topic_score_gemma":0.00211349,"domain_scores_codex":[0.9957076,0.002323062,0.0001624624,0.0005515999,0.0009325922,0.0003226046],"domain_scores_gemma":[0.984172,0.009251799,0.001173892,0.002973793,0.002099119,0.0003293548],"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":[0.0006159261,0.0003938078,0.01511127,0.0001780758,0.000209063,0.0001224738,0.0002024923,0.3315426,0.006923264,0.02352554,0.006508561,0.614667],"study_design_scores_gemma":[0.00005827039,0.0002778611,0.002058716,0.0000604678,0.000020166,0.000143538,0.00017346,0.9392369,0.009245183,0.04514083,0.003550672,0.00003389378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2578408,0.001430107,0.713665,0.01582434,0.0004163092,0.00009320202,0.0002079786,0.002638086,0.007884136],"genre_scores_gemma":[0.8204862,0.0004309199,0.1756424,0.001217515,0.0001663476,0.00005757522,0.0002505685,0.0002050436,0.001543414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009427527,"threshold_uncertainty_score":0.04985815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02070309062003079,"score_gpt":0.2229412609384668,"score_spread":0.2022381703184361,"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."}}