{"id":"W2167208964","doi":"10.1109/cnsr.2008.75","title":"Detecting Network Anomalies Using Different Wavelet Basis Functions","year":2008,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Wavelet; Computer science; Intrusion detection system; Anomaly detection; Data mining; Artificial intelligence; Network packet; Basis (linear algebra); Outlier; Wavelet packet decomposition; Pattern recognition (psychology); Wavelet transform; Autoregressive model; Machine learning; Mathematics; Computer security","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00131049,0.000575921,0.0007170092,0.002862233,0.00023127,0.0008126929,0.0004334418,0.0004953954,0.0003004106],"category_scores_gemma":[0.003909017,0.0001453008,0.0005345489,0.001959436,0.000279862,0.001034751,0.0004820406,0.0006639925,0.0001401117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003277749,"about_ca_system_score_gemma":0.0003001604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001033031,"about_ca_topic_score_gemma":0.0009519474,"domain_scores_codex":[0.9993764,0.0001236635,0.00005013021,0.0000952508,0.0002873169,0.00006729276],"domain_scores_gemma":[0.9983692,0.0006722029,0.0002505236,0.0001462892,0.0004942834,0.00006754071],"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":[0.0007770885,0.0005233662,0.04193247,0.000200922,0.0002262912,0.0002364608,0.0002231985,0.1294566,0.09573956,0.005114729,0.001158391,0.7244108],"study_design_scores_gemma":[0.0000244451,0.0002421379,0.01855899,0.00001531688,0.00006903535,0.000191785,0.00008997838,0.9535533,0.02387683,0.00247719,0.0008655905,0.00003537581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5460234,0.0002938046,0.4517139,0.0001595756,0.0000508657,0.00005563096,0.0001941548,0.0006208488,0.0008878589],"genre_scores_gemma":[0.8511336,0.000320862,0.1477372,0.00002186663,0.00002999659,0.00004346186,0.0003469005,0.00004318945,0.0003228683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002862233,"threshold_uncertainty_score":0.00693059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0334611042225015,"score_gpt":0.2228818951983506,"score_spread":0.189420790975849,"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."}}