{"id":"W4246413350","doi":"10.32920/ryerson.14647329","title":"Detection and mitigation of SYN and HTTP flood DDoS attacks in software defined networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Advanced Research Projects Agency; Defense Advanced Research Projects Agency","keywords":"Denial-of-service attack; Internet Control Message Protocol; Computer science; Spoofing attack; Computer security; Application layer DDoS attack; Flood myth; Computer network; Network security; Network packet; The Internet; Geography","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":[0.0003290777,0.0001981196,0.0003163922,0.0001827668,0.00008045162,0.0002054877,0.0002030082,0.000416157,0.00001328992],"category_scores_gemma":[0.0000944517,0.0002072167,0.00005674574,0.000341426,0.00005388527,0.0003053478,0.0009614794,0.0005370359,5.758988e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000404804,"about_ca_system_score_gemma":0.00004932635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002638093,"about_ca_topic_score_gemma":0.001266338,"domain_scores_codex":[0.9984842,0.0001434357,0.0003954584,0.0006051649,0.0001866566,0.0001851012],"domain_scores_gemma":[0.9990415,0.0001239394,0.0002534103,0.0003936653,0.0001183476,0.00006915568],"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":[0.00006730676,0.0001878739,0.008845835,0.0008088189,0.0001040785,0.00003184511,0.002196642,0.03107642,0.00190981,0.002950799,0.0001103074,0.9517103],"study_design_scores_gemma":[0.0004761312,0.0001297066,0.02963116,0.0004747621,0.00002194111,0.0000492177,0.00006062181,0.9582632,0.005787914,0.004672434,0.00007263724,0.0003602958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4836315,0.00101964,0.5144307,0.00008158298,0.0005079959,0.0001868599,7.755791e-7,0.00007439265,0.00006656401],"genre_scores_gemma":[0.9804827,0.0009717703,0.01830869,0.00008110376,0.00008626599,0.00002813357,0.000009512686,0.000009433712,0.00002238484],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.95135,"threshold_uncertainty_score":0.845005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009324662901209091,"score_gpt":0.217869884431685,"score_spread":0.2085452215304759,"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."}}