{"id":"W4210445337","doi":"10.1109/tnsm.2022.3142254","title":"An Online Entropy-Based DDoS Flooding Attack Detection System With Dynamic Threshold","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Denial-of-service attack; Application layer DDoS attack; Entropy (arrow of time); Network packet; Computer security; Computer network; Server; Intrusion detection system; Flooding (psychology); The Internet; Real-time computing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000671313,0.0006307831,0.001004247,0.001808824,0.0004254883,0.0006274299,0.000973196,0.0005539847,0.0008202642],"category_scores_gemma":[0.001965309,0.0002745905,0.0002489034,0.0006865007,0.0003036628,0.001360169,0.0008330339,0.0004501815,0.0003222337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006485163,"about_ca_system_score_gemma":0.0005335505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001539684,"about_ca_topic_score_gemma":0.001083846,"domain_scores_codex":[0.9994091,0.00007329914,0.00006481032,0.0001662318,0.0002334924,0.00005308256],"domain_scores_gemma":[0.9991217,0.000246303,0.0001285401,0.000134408,0.0002769608,0.00009208483],"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":[0.002602451,0.001567865,0.03055603,0.0003155965,0.0002926555,0.001207232,0.0004669069,0.1211255,0.1467395,0.004185604,0.008396445,0.6825442],"study_design_scores_gemma":[0.0000491713,0.0001887013,0.004633887,0.000005728488,0.00003080773,0.0002712781,0.00001472321,0.9677573,0.0250768,0.0009016992,0.001033224,0.00003675848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4420645,0.0004511613,0.521848,0.0004039215,0.0001483897,0.0004747137,0.000695112,0.03049101,0.00342326],"genre_scores_gemma":[0.9392357,0.00007385528,0.05887381,0.00009754506,0.00003812724,0.0001062028,0.0004078948,0.00005976809,0.001106993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001808824,"threshold_uncertainty_score":0.00470531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01418457558909416,"score_gpt":0.2181636561129993,"score_spread":0.2039790805239052,"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."}}