{"id":"W2144870254","doi":"10.1109/compsac.2006.40","title":"Efficacy of Hidden Markov Models Over Neural Networks in Anomaly Intrusion Detection","year":2006,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Saudi Aramco","keywords":"Hidden Markov model; Anomaly detection; Computer science; Intrusion detection system; Anomaly (physics); Artificial neural network; Artificial intelligence; Markov model; Markov chain; Machine learning; Data mining; Pattern recognition (psychology)","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":[0.0002490452,0.000133483,0.0001737587,0.0002137056,0.00007378204,0.00006319739,0.0003468834,0.0001331014,0.0000478655],"category_scores_gemma":[0.000008590483,0.0001239054,0.00007824248,0.0007977893,0.00003171432,0.0007875546,0.0002120984,0.0002035911,0.000003472547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005383167,"about_ca_system_score_gemma":0.00001109804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001547013,"about_ca_topic_score_gemma":0.001032003,"domain_scores_codex":[0.9986892,0.00009871878,0.0003897282,0.0003338935,0.0002280254,0.0002604368],"domain_scores_gemma":[0.99936,0.00008515952,0.0001190771,0.0003484811,0.00004675138,0.00004057636],"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":[0.0002883388,0.0002948629,0.001663692,0.00001358994,0.00000801445,0.00001253283,0.0001476059,0.2496717,0.003262491,0.01619296,0.0005404538,0.7279037],"study_design_scores_gemma":[0.0005840891,0.00009852864,0.03871545,0.00001323287,0.000002474384,0.000008783977,0.00000239548,0.9539217,0.002386651,0.004056334,0.00007798854,0.0001323855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5639067,0.00006729503,0.433551,0.00004615676,0.0003580205,0.0001306284,1.577819e-7,0.00009208973,0.001847859],"genre_scores_gemma":[0.9964851,0.00001912589,0.00310604,0.00008909242,0.0001849461,0.00000643383,0.000001682502,0.000007848113,0.00009971231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7277713,"threshold_uncertainty_score":0.5052716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008286307470746048,"score_gpt":0.209827457044618,"score_spread":0.201541149573872,"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."}}