{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008708609,0.001118078,0.00105361,0.00106619,0.0005339345,0.001399073,0.0008248574,0.001406332,0.0010042],"category_scores_gemma":[0.02685907,0.0006487735,0.0005917386,0.0006264073,0.0009964667,0.003323277,0.0009928448,0.001330384,0.0002546699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001290279,"about_ca_system_score_gemma":0.001117356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007304167,"about_ca_topic_score_gemma":0.006058596,"domain_scores_codex":[0.9973412,0.001566498,0.0001163644,0.0002817545,0.0005145312,0.0001795967],"domain_scores_gemma":[0.9725149,0.02438215,0.001046317,0.0007790077,0.001030167,0.0002474443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008052261,0.0001679586,0.005935118,0.0001020811,0.0001351738,0.00009657034,0.00008443974,0.910707,0.001737394,0.01085881,0.0004847665,0.06888552],"study_design_scores_gemma":[0.000009115149,0.00004604196,0.0002759295,0.000006009492,0.00001398798,0.00001429099,0.00000464336,0.9959521,0.0005341522,0.003080701,0.00005569664,0.000007278189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2759759,0.003347899,0.7125463,0.001705365,0.0001626784,0.0000825247,0.0001727859,0.001459358,0.004547215],"genre_scores_gemma":[0.9554045,0.0008830094,0.04225338,0.0001143369,0.00008741017,0.00003444323,0.00008399599,0.00003686567,0.001102169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008708609,"threshold_uncertainty_score":0.04605609,"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."}}