{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002970602,0.000145555,0.0001938448,0.0002206086,0.000351446,0.0002277586,0.0002080861,0.000126236,0.00002133283],"category_scores_gemma":[0.0001923282,0.0001456731,0.0000867715,0.0007043558,0.00005358816,0.002816966,0.0001370871,0.0002236655,0.00001720576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002339889,"about_ca_system_score_gemma":0.00005720444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007445159,"about_ca_topic_score_gemma":0.00002942797,"domain_scores_codex":[0.998556,0.0001578977,0.0005889821,0.0001686346,0.0003235229,0.0002049626],"domain_scores_gemma":[0.9985744,0.00005453505,0.0005984182,0.0002921017,0.0004212627,0.00005923716],"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":[0.00003425243,0.00008516209,0.002015726,0.0003279722,0.00005569038,0.000003124683,0.005697221,0.004937859,0.1012898,0.007317265,0.00001245774,0.8782234],"study_design_scores_gemma":[0.0002351979,0.0000871283,0.004246024,0.00008588417,0.00001045656,0.0000689475,0.0002121789,0.8582578,0.1339976,0.001573268,0.001076212,0.0001493835],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3343669,0.0001191826,0.6642733,0.00001996678,0.0004764445,0.0001060015,0.000002243698,0.0001299002,0.0005060799],"genre_scores_gemma":[0.9931768,0.00008172251,0.006545195,0.00004135785,0.00006682549,0.00001254465,0.00004495982,0.000006719326,0.00002386548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.878074,"threshold_uncertainty_score":0.5940375,"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."}}