{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00185309,0.001179927,0.001628112,0.002645914,0.0006813821,0.001212779,0.002498034,0.001555817,0.002313356],"category_scores_gemma":[0.003306898,0.0004682923,0.001548175,0.002217643,0.0007885758,0.002719085,0.001593477,0.002198112,0.001509527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000884963,"about_ca_system_score_gemma":0.001644535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003368477,"about_ca_topic_score_gemma":0.002965955,"domain_scores_codex":[0.9962447,0.0005657889,0.0002099974,0.0006250654,0.002150131,0.0002042534],"domain_scores_gemma":[0.9979886,0.0003394832,0.0001224123,0.0004948745,0.0009765253,0.00007814191],"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":[0.0002433663,0.0003274821,0.002489368,0.0002081209,0.0002639306,0.0002949216,0.000129706,0.06755063,0.0336108,0.0410295,0.008973305,0.8448789],"study_design_scores_gemma":[0.00003697946,0.0001736136,0.0008654132,0.00001726185,0.00009842248,0.0006083709,0.00002076375,0.9476616,0.01491976,0.0190906,0.0164452,0.00006204884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002928987,0.0002694685,0.9941919,0.000171797,0.0001338182,0.00008776992,0.00006669941,0.001386801,0.0007627099],"genre_scores_gemma":[0.1113596,0.0004418905,0.8807132,0.0004056576,0.0002161824,0.0001836815,0.0003863384,0.0001684694,0.006124976],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003368477,"threshold_uncertainty_score":0.009800196,"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."}}