{"id":"W3081430061","doi":"10.1109/access.2020.3019330","title":"A Flexible SDN-Based Architecture for Identifying and Mitigating Low-Rate DDoS Attacks Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":304,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"CYTED Ciencia y Tecnología para el Desarrollo; Instituto Tecnológico y de Estudios Superiores de Monterrey; University of Texas at San Antonio","keywords":"Computer science; Denial-of-service attack; Intrusion detection system; Software-defined networking; Support vector machine; Application layer DDoS attack; Computer network; Testbed; Random forest; Machine learning; Artificial intelligence; Computer security; Operating system; The Internet","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002750519,0.0001530914,0.0001782237,0.00008681657,0.0004971908,0.0007479569,0.0005105186,0.00007042762,0.0000107168],"category_scores_gemma":[0.0001195864,0.0001521119,0.00006685893,0.0004752929,0.00003458075,0.0006780392,0.0001898377,0.0003262717,0.000002918229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002042395,"about_ca_system_score_gemma":0.00003971497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007436163,"about_ca_topic_score_gemma":0.00002994079,"domain_scores_codex":[0.9987888,0.0001049341,0.000224185,0.0004402997,0.0001594912,0.0002822308],"domain_scores_gemma":[0.9992645,0.0001830795,0.0001999693,0.0001595833,0.00006557428,0.0001272769],"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":[0.0002474372,0.00007277328,0.004643091,0.001400167,0.00008584309,0.00005063345,0.00653589,0.5722178,0.266778,0.0006533578,0.0003845742,0.1469304],"study_design_scores_gemma":[0.0004385624,0.00007173372,0.00009722057,0.0001074274,0.000009076248,0.000006202572,0.00001209788,0.8785623,0.1189535,0.0008857449,0.0006824312,0.0001736506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3957322,0.0001097897,0.6030135,0.0005555499,0.0002423857,0.0001685982,0.000002002761,0.0001561384,0.00001989794],"genre_scores_gemma":[0.9794614,0.000006520331,0.01847541,0.001686731,0.0003207239,0.00001620612,0.000003349265,0.0000182451,0.00001145939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.584538,"threshold_uncertainty_score":0.7212566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07119681957159003,"score_gpt":0.3258249427423021,"score_spread":0.2546281231707121,"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."}}