{"id":"W1606225312","doi":"","title":"An Improved Approach for Signature and Anomaly based Intrusion Detection and Prevention","year":2012,"lang":"en","type":"article","venue":"Computational intelligence","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Intrusion detection system; Anomaly-based intrusion detection system; Anomaly detection; Signature (topology); Computer science; Misuse detection; Anomaly (physics); Data mining; Intrusion; Artificial intelligence; Pattern recognition (psychology); Geology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004393306,0.00012482,0.0001062807,0.0001153803,0.0002731914,0.0001404377,0.0001666617,0.0001092597,0.000005149872],"category_scores_gemma":[0.00003670184,0.0001225156,0.00003344287,0.0002280287,0.00005449172,0.001015585,0.00006589856,0.0001291315,0.000001325749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002765703,"about_ca_system_score_gemma":0.00002438996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007744243,"about_ca_topic_score_gemma":0.000003982163,"domain_scores_codex":[0.9990492,0.00008146716,0.0002004296,0.0003343073,0.000138817,0.0001957052],"domain_scores_gemma":[0.9993419,0.0001498972,0.0001000374,0.0001441487,0.000137451,0.0001265469],"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":[0.000108077,0.0002511273,0.000280045,0.00008594838,0.00001544497,2.094095e-7,0.000747879,0.04227941,0.006349287,0.02693598,0.00002679333,0.9229198],"study_design_scores_gemma":[0.0001193095,0.0003395064,0.002513175,0.00001031823,0.000006873556,0.00001730784,0.00002872547,0.970068,0.00632763,0.02021811,0.0002035932,0.0001474154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04172446,0.0004030916,0.9570694,0.00005798068,0.0002459113,0.0003675321,0.000001967761,0.00009317048,0.00003647825],"genre_scores_gemma":[0.7486515,0.00001005931,0.2510067,0.0001343183,0.0001315832,0.0000370102,0.00001508269,0.000005869032,0.000007872917],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9277886,"threshold_uncertainty_score":0.4996042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02219245460922694,"score_gpt":0.2746945201739298,"score_spread":0.2525020655647029,"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."}}