{"id":"W2153919695","doi":"10.1109/icc.2006.255127","title":"Anomaly Based Network Intrusion Detection with Unsupervised Outlier Detection","year":2006,"lang":"en","type":"article","venue":"2006 IEEE International Conference on Communications","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":247,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Anomaly detection; Outlier; Computer science; Anomaly (physics); Intrusion detection system; Data mining; Random forest; Artificial intelligence; Anomaly-based intrusion detection system; Pattern recognition (psychology); Machine learning","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":[0.002051475,0.0009651667,0.001711281,0.00349976,0.0005586713,0.001244987,0.001834824,0.00105228,0.0004880567],"category_scores_gemma":[0.007822873,0.0003880497,0.00112517,0.003058861,0.0007253084,0.002071868,0.001555179,0.001245094,0.0004202378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005695085,"about_ca_system_score_gemma":0.0008350132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00164783,"about_ca_topic_score_gemma":0.002064639,"domain_scores_codex":[0.9960814,0.0008660713,0.0003227611,0.000793588,0.001686192,0.0002500452],"domain_scores_gemma":[0.9942965,0.001944462,0.001008211,0.001061131,0.001548356,0.0001413946],"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":[0.0004834411,0.0007384068,0.03406646,0.0002641081,0.0005804725,0.0004952576,0.0002224949,0.1819373,0.02500642,0.007480446,0.007069998,0.7416552],"study_design_scores_gemma":[0.0000219885,0.0001229711,0.004263192,0.00001193174,0.00003866163,0.0005299633,0.0000401174,0.9731232,0.01243059,0.006935985,0.00244694,0.00003441344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03101785,0.0001809285,0.963796,0.0001220492,0.00004999587,0.0001353843,0.0002231181,0.003902522,0.0005722256],"genre_scores_gemma":[0.4343913,0.0002023483,0.5623965,0.0001161515,0.0001032033,0.0002738424,0.001332128,0.0001383253,0.001046162],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00349976,"threshold_uncertainty_score":0.01084936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0376647273698407,"score_gpt":0.2678853771644259,"score_spread":0.2302206497945852,"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."}}