{"id":"W1556616707","doi":"10.1007/978-3-642-04091-7_12","title":"An Investigation of Multi-objective Genetic Algorithms for Encrypted Traffic Identification","year":2009,"lang":"en","type":"book-chapter","venue":"Advances in intelligent and soft computing","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Identification (biology); Encryption; Computer science; Payload (computing); Cluster analysis; Genetic algorithm; Task (project management); Selection (genetic algorithm); Port (circuit theory); Data mining; Feature selection; Cluster (spacecraft); Algorithm; Machine learning; Computer network; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005181489,0.0003119925,0.0004860788,0.0003610303,0.0001137363,0.0001039574,0.0005498365,0.0001918539,0.000002674624],"category_scores_gemma":[0.00004357864,0.0003215347,0.0001463753,0.0001190298,0.0001136884,0.0003629726,0.0000674587,0.0002525522,0.000002205105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007063511,"about_ca_system_score_gemma":0.00004651168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003744864,"about_ca_topic_score_gemma":0.0000594569,"domain_scores_codex":[0.9977517,0.00004694494,0.0009638569,0.0007365231,0.0002513497,0.0002495938],"domain_scores_gemma":[0.9984663,0.0002314699,0.0006930846,0.0002375277,0.0002981431,0.00007343526],"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":[0.000005649866,0.00002900768,0.00003475469,0.00006910559,0.00003173182,0.000002793622,0.003994443,0.1209664,0.00003704668,0.08362006,0.000002433424,0.7912066],"study_design_scores_gemma":[0.0001700928,0.0001500345,0.0001852069,0.0003298088,0.00003166341,0.000004907311,0.0001136313,0.9953495,0.0002915333,0.002746859,0.0003052918,0.0003214988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00788841,0.004246924,0.9867541,0.00001537524,0.0003336399,0.0004219066,0.000003888439,0.00007674113,0.0002590838],"genre_scores_gemma":[0.895304,0.0005291162,0.1029653,0.00004077454,0.0001541458,0.00000622606,0.00004374176,0.00002439946,0.0009322729],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8874156,"threshold_uncertainty_score":0.9999236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0230334190034745,"score_gpt":0.2851165836281543,"score_spread":0.2620831646246798,"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."}}