{"id":"W3018321377","doi":"10.1109/smartnets48225.2019.9069788","title":"Evaluation of Deep Learning in Detecting Unknown Network Attacks","year":2019,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Benchmark (surveying); Artificial intelligence; Deep learning; Machine learning; Focus (optics); Denial-of-service attack; Sophistication; Binary classification; Artificial neural network; False positive rate; Data mining; Support vector machine; The Internet","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":[0.003055068,0.00006580405,0.0001127915,0.00008456211,0.00005370597,0.00003336708,0.0002244129,0.00006267695,0.0002296454],"category_scores_gemma":[0.0001033224,0.00006337291,0.00003481257,0.0006362211,0.000008416586,0.0003435682,0.0001232019,0.0001971219,0.00006076909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005944627,"about_ca_system_score_gemma":0.00003231383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000429001,"about_ca_topic_score_gemma":0.0003167007,"domain_scores_codex":[0.998593,0.0003093096,0.0002274925,0.0002173687,0.000466108,0.0001866837],"domain_scores_gemma":[0.9993675,0.000113805,0.0001068507,0.0002130076,0.0001753524,0.00002352878],"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":[0.000003898302,0.00001290896,0.005611659,0.000005598711,0.000004742121,2.253715e-7,0.0003482054,0.3344523,0.0004687405,0.004098715,0.00001366612,0.6549793],"study_design_scores_gemma":[0.0002999944,0.00008953146,0.006725568,0.00002865343,0.000004277982,0.000002313155,0.00002929191,0.9873393,0.001435437,0.003314454,0.0006531769,0.00007804079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8367037,0.0002358143,0.1413843,0.00005106689,0.000611837,0.0002187948,1.011162e-8,0.00009244536,0.02070202],"genre_scores_gemma":[0.9959295,0.00001061511,0.003841562,0.00004026566,0.00006946425,0.000005400827,2.787826e-7,0.000003873145,0.00009903637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6549013,"threshold_uncertainty_score":0.2584273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01994467954226293,"score_gpt":0.267123044602555,"score_spread":0.2471783650602921,"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."}}