{"id":"W2799960805","doi":"10.1109/iscas.2018.8351647","title":"Evaluation of Support Vector Machine Kernels for Detecting Network Anomalies","year":2018,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Support vector machine; Border Gateway Protocol; Computer science; Anomaly detection; Convergence (economics); Routing protocol; Distance-vector routing protocol; Anomaly (physics); Default-free zone; Machine learning; Artificial intelligence; The Internet; Data mining; Routing (electronic design automation); Computer network; Static routing","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":[0.002501506,0.00008188446,0.00011776,0.00005070254,0.0001821209,0.00004581439,0.0002735657,0.00005382848,0.0002940971],"category_scores_gemma":[0.0001327039,0.00007281917,0.00005962135,0.0003078911,0.00004122415,0.0003034605,0.0001022674,0.00004814166,0.0000187613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003382614,"about_ca_system_score_gemma":0.0000666293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002936145,"about_ca_topic_score_gemma":0.0003578727,"domain_scores_codex":[0.9988263,0.0001031418,0.0002423775,0.0002293803,0.0003941527,0.0002046284],"domain_scores_gemma":[0.9988419,0.0001033138,0.0001328508,0.0002828992,0.0006020403,0.00003697263],"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":[0.00007536766,0.00008629417,0.0007941119,0.00003396364,0.00008354544,4.061943e-7,0.001311279,0.001950581,0.003923314,0.04396531,0.007551727,0.9402241],"study_design_scores_gemma":[0.0004022034,0.0006482131,0.001266867,0.00001368851,0.00002849028,0.000006607072,0.00001017786,0.9325985,0.04275564,0.01697851,0.005175224,0.0001158468],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1221027,0.0001077899,0.8688145,0.0001707101,0.001431722,0.0004326924,0.000001621105,0.0001419803,0.006796254],"genre_scores_gemma":[0.9731569,0.000002999434,0.02590095,0.0001295206,0.0006279454,0.00002641026,0.00000155937,0.000005866636,0.0001478753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9401082,"threshold_uncertainty_score":0.3220156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03977164240815087,"score_gpt":0.2956290652907381,"score_spread":0.2558574228825872,"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."}}