{"id":"W3217612788","doi":"10.18280/isi.260505","title":"Detection of Different DDoS Attacks Using Machine Learning Classification Algorithms","year":2021,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Denial-of-service attack; Computer science; Application layer DDoS attack; Naive Bayes classifier; Random forest; Trinoo; Machine learning; Statistical classification; Algorithm; Decision tree; Artificial intelligence; Reputation; Network security; Computer security; Data mining; Support vector machine; The Internet; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002727766,0.001637071,0.001639919,0.007701807,0.0008346774,0.0018187,0.001389311,0.001472671,0.0007379671],"category_scores_gemma":[0.007354361,0.0003205906,0.00133143,0.003496211,0.0004030635,0.001885957,0.0006922164,0.001280514,0.0005392632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001403352,"about_ca_system_score_gemma":0.001193029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007558053,"about_ca_topic_score_gemma":0.004494237,"domain_scores_codex":[0.9969708,0.000455097,0.0005589003,0.0006537816,0.001029318,0.0003320299],"domain_scores_gemma":[0.9948094,0.002250714,0.0007431793,0.0004319157,0.001623634,0.0001411036],"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":[0.0006277075,0.001958166,0.06570719,0.0005041872,0.0004520328,0.0004077091,0.0001915183,0.2629626,0.008990787,0.002553878,0.0120387,0.6436055],"study_design_scores_gemma":[0.00003508606,0.0001954045,0.009713952,0.00004492847,0.00005300232,0.000138454,0.0001163974,0.975675,0.009311846,0.00190096,0.002773779,0.00004123017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5918116,0.003172325,0.3783124,0.001585976,0.0007762285,0.001177726,0.00567566,0.008904246,0.008583848],"genre_scores_gemma":[0.7863576,0.0008061705,0.2034171,0.0001909175,0.0001127424,0.0003718621,0.006746443,0.00007154542,0.001925486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007701807,"threshold_uncertainty_score":0.01502812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02423880062721817,"score_gpt":0.2436767125884133,"score_spread":0.2194379119611951,"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."}}