{"id":"W2125281248","doi":"10.1109/icc.2007.234","title":"The Power of Temporal Pattern Processing in Anomaly Intrusion Detection","year":2007,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Saudi Aramco","keywords":"Generalization; Computer science; Anomaly detection; Intrusion detection system; Artificial intelligence; Recurrent neural network; Machine learning; Artificial neural network; Perceptron; Pattern recognition (psychology); False positive rate; Anomaly-based intrusion detection system; Multilayer perceptron; Anomaly (physics); Mathematics","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":[0.001872232,0.0007053014,0.0007160341,0.001486543,0.0003362473,0.001333701,0.0008206356,0.0007481202,0.001260766],"category_scores_gemma":[0.007079351,0.0003461876,0.0006812718,0.00155265,0.0009703679,0.002515187,0.0007784242,0.001038062,0.0005327676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004591967,"about_ca_system_score_gemma":0.0005719698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001710401,"about_ca_topic_score_gemma":0.001217852,"domain_scores_codex":[0.9984306,0.0003607345,0.0001467221,0.0003250364,0.0006547862,0.00008210984],"domain_scores_gemma":[0.9959109,0.002326613,0.0004473248,0.0005517512,0.0006947099,0.0000686589],"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":[0.0002830885,0.0001230209,0.009377935,0.0004215156,0.0001825444,0.0004341272,0.0001741241,0.09691063,0.03716106,0.0259283,0.002423911,0.8265797],"study_design_scores_gemma":[0.00002375944,0.0004390848,0.004851062,0.00008178036,0.0001189939,0.001591799,0.00008795894,0.91483,0.02524573,0.04464799,0.008007276,0.0000744434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03053326,0.002144612,0.9614768,0.0005726978,0.000142307,0.00005827459,0.0001420851,0.001043322,0.003886586],"genre_scores_gemma":[0.6742676,0.004090026,0.318411,0.0003174626,0.000361983,0.00009427701,0.0003217574,0.0001060393,0.002029918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001872232,"threshold_uncertainty_score":0.009901404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008455556830784786,"score_gpt":0.2324400218795717,"score_spread":0.2239844650487869,"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."}}