{"id":"W2121741220","doi":"10.1109/pst.2011.5971981","title":"Data preprocessing for distance-based unsupervised Intrusion Detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Mahalanobis distance; Normalization (sociology); Euclidean distance; Computer science; Pattern recognition (psychology); Intrusion detection system; Artificial intelligence; Preprocessor; Data mining; Outlier; Anomaly detection; Curse of dimensionality; Principal component analysis; Feature extraction; Data pre-processing; Distance measures","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":[0.001273194,0.00121861,0.001047497,0.002967529,0.0008896836,0.001222328,0.001299727,0.0005348981,0.002384235],"category_scores_gemma":[0.007911583,0.0004098432,0.0009130982,0.003685086,0.0005228798,0.001475784,0.001006526,0.00137409,0.00214548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006526887,"about_ca_system_score_gemma":0.001475109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002342916,"about_ca_topic_score_gemma":0.00250974,"domain_scores_codex":[0.9979064,0.0002666815,0.0002958642,0.0004625304,0.0009394406,0.0001290355],"domain_scores_gemma":[0.9958357,0.001093201,0.0003941074,0.0007712503,0.001830521,0.00007521607],"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":[0.0003736535,0.0005690707,0.008485021,0.0005842611,0.0001397603,0.000243318,0.0005239665,0.02117511,0.07762094,0.004096394,0.008139063,0.8780495],"study_design_scores_gemma":[0.00007818689,0.0009542084,0.0434317,0.0001537541,0.0001205687,0.0009808746,0.0009526077,0.5858907,0.3002301,0.01027497,0.05667888,0.0002534075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05018119,0.0002220248,0.9379113,0.0001840759,0.0001579713,0.0008059179,0.001661751,0.007007591,0.00186815],"genre_scores_gemma":[0.1872795,0.0002880159,0.8041186,0.00007715364,0.0000407955,0.001242011,0.004944321,0.0003385207,0.001671105],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002967529,"threshold_uncertainty_score":0.007976115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1030304552684645,"score_gpt":0.2903539398748656,"score_spread":0.1873234846064011,"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."}}