{"id":"W3010162966","doi":"10.23919/cnsm46954.2019.9012662","title":"A Framework & System for Classification of Encrypted Network Traffic using Machine Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Solana Networks (Canada)","funders":"","keywords":"Traffic classification; Computer science; Artificial intelligence; Encryption; Deep packet inspection; Machine learning; Binary classification; Software deployment; Traffic generation model; Support vector machine; Identification (biology); Network packet; Ground truth; Data mining; Real-time computing; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006374056,0.0001302706,0.0002983163,0.00008636764,0.00009926766,0.00008411943,0.0004604404,0.000110615,0.00003367863],"category_scores_gemma":[0.00005022293,0.0001102128,0.0001725805,0.0004393338,0.00001463233,0.0001465279,0.00006519765,0.0001798003,0.0000321835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005394637,"about_ca_system_score_gemma":0.00003411042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001368827,"about_ca_topic_score_gemma":0.00002808035,"domain_scores_codex":[0.9986415,0.00009388414,0.0004305161,0.0003483862,0.000214523,0.000271188],"domain_scores_gemma":[0.9989958,0.0002661404,0.0002922805,0.000251634,0.0001488913,0.00004525766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009768857,0.00002120894,0.0007146552,0.00007797436,0.00005305251,2.427725e-7,0.0005799723,0.4777858,0.0006742554,0.5180649,0.00003398736,0.001984258],"study_design_scores_gemma":[0.0001477009,0.00004582592,0.00009513016,0.0001556256,0.00002190388,0.000004219304,0.0001296666,0.9984564,0.00004809893,0.00001780092,0.0007515211,0.0001261718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2714042,0.0000797481,0.7276443,0.00003078498,0.0002726634,0.0001714624,4.53036e-7,0.0001331239,0.0002632076],"genre_scores_gemma":[0.8279466,0.000001184325,0.1716658,0.0000224826,0.0000968931,0.000004406415,0.000007483514,0.00001026821,0.0002448421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5565424,"threshold_uncertainty_score":0.4494346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03094149336751063,"score_gpt":0.2680108568899983,"score_spread":0.2370693635224876,"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."}}