{"id":"W2970145645","doi":"10.1109/services.2019.00051","title":"DDoS Attacks Detection and Mitigation in SDN Using Machine Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":136,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Carleton University","funders":"","keywords":"Denial-of-service attack; Computer science; Software-defined networking; Scalability; C4.5 algorithm; Computer security; Computer network; Support vector machine; Process (computing); Flexibility (engineering); Network security; Block (permutation group theory); Machine learning; Naive Bayes classifier; Operating system; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001704985,0.0006337557,0.0008528665,0.001167597,0.00045976,0.0006532167,0.0006230316,0.0006425345,0.0004262314],"category_scores_gemma":[0.002829689,0.0002409218,0.0004909381,0.0005855882,0.0003106164,0.00116365,0.0003317559,0.0005489544,0.0001245417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006678365,"about_ca_system_score_gemma":0.0006654324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003917344,"about_ca_topic_score_gemma":0.003338209,"domain_scores_codex":[0.9991245,0.000226858,0.00008355132,0.0001605972,0.0002945015,0.000109999],"domain_scores_gemma":[0.9979885,0.0009241258,0.00026776,0.0001577618,0.0005834016,0.00007843346],"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":[0.0005280933,0.0005960746,0.02262406,0.0001647373,0.0001745758,0.0001739027,0.00009661685,0.7070273,0.01299083,0.0009379921,0.001116559,0.2535693],"study_design_scores_gemma":[0.000007379504,0.0000902416,0.00140284,0.000005728809,0.00001080858,0.00002367611,0.00001195534,0.9935803,0.004380989,0.0003212796,0.0001579876,0.00000681435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5654146,0.0008269567,0.4266447,0.0004548334,0.0001562765,0.0001753734,0.0001758221,0.003503715,0.002647701],"genre_scores_gemma":[0.9448869,0.000148792,0.05433603,0.00004126208,0.00001616323,0.0000352375,0.0001100445,0.00001752852,0.0004079815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003917344,"threshold_uncertainty_score":0.009016991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009771176493239197,"score_gpt":0.2272586635484969,"score_spread":0.2174874870552577,"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."}}