{"id":"W4295836371","doi":"10.48550/arxiv.2105.14564","title":"Evaluating Resilience of Encrypted Traffic Classification Against Adversarial Evasion Attacks","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Deep learning; Convolutional neural network; Adversarial machine learning; Adversarial system; Machine learning; Evasion (ethics); Encryption; Decision tree; Traffic classification; Resilience (materials science); The Internet; Computer security","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00306758,0.0009240439,0.0006437534,0.001724324,0.0003855965,0.0009084637,0.0005540926,0.001105715,0.000823312],"category_scores_gemma":[0.01764131,0.0001735945,0.000440775,0.0007076211,0.0009164975,0.001476196,0.001069619,0.00106318,0.0003207551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007997318,"about_ca_system_score_gemma":0.000523594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002140468,"about_ca_topic_score_gemma":0.001149441,"domain_scores_codex":[0.9979049,0.0006576537,0.0001971723,0.0003136801,0.0006053363,0.0003213188],"domain_scores_gemma":[0.9868123,0.008041988,0.001570028,0.001991713,0.001022248,0.0005617171],"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":[0.002389577,0.000820084,0.04105016,0.0002134623,0.0003654535,0.0002126593,0.0001171944,0.8487757,0.01434381,0.004427208,0.002982051,0.08430266],"study_design_scores_gemma":[0.00002396206,0.0005042856,0.005722929,0.00002157685,0.00003170875,0.0001048507,0.00006716276,0.9785388,0.0126285,0.001924895,0.0004123009,0.0000189789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9761397,0.0005689096,0.01937042,0.0003558623,0.0001111303,0.00007348505,0.0003127208,0.0007045022,0.002363288],"genre_scores_gemma":[0.9961407,0.00008801587,0.003087131,0.00003304394,0.00001743152,0.00001128015,0.0002950771,0.00001876919,0.0003084689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00306758,"threshold_uncertainty_score":0.01622313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1153685052425876,"score_gpt":0.2498503531899524,"score_spread":0.1344818479473648,"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."}}