{"id":"W2041144750","doi":"10.1109/glocom.2011.6134252","title":"Rule Mode Selection in Intrusion Detection and Prevention Systems","year":2011,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Korea Science and Engineering Foundation","keywords":"Intrusion detection system; Computer science; Usability; Computer security; Network security; Network packet; Enforcement; Mode (computer interface); Selection (genetic algorithm); Computer network; Artificial intelligence","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":[0.000268934,0.00008120124,0.00008822434,0.0001917766,0.0001049623,0.00006941477,0.0001110435,0.00009304268,0.00002246756],"category_scores_gemma":[0.00000986434,0.00007685943,0.00001979932,0.0004158904,0.00001257241,0.0008045423,0.00008146896,0.0001229008,0.00001708612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005388299,"about_ca_system_score_gemma":0.00001139117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001078183,"about_ca_topic_score_gemma":0.0008461769,"domain_scores_codex":[0.9991812,0.0001003185,0.0001933846,0.0002639176,0.0001160536,0.0001451091],"domain_scores_gemma":[0.9997408,0.00001212528,0.00006075502,0.0001064356,0.00003716557,0.00004275989],"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":[0.0001306975,0.000266273,0.002442962,0.00007607751,0.00001828709,0.000005816975,0.003459475,0.0008763888,0.04717158,0.08207931,0.0001280026,0.8633451],"study_design_scores_gemma":[0.0003462548,0.000295882,0.01351045,0.0000416887,0.000003452129,0.0000652016,0.00005478259,0.9384601,0.02360881,0.02299758,0.0004442465,0.0001716007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4308034,0.00005614437,0.5658638,0.00001250871,0.0003550661,0.0001595884,6.375146e-8,0.0001475905,0.002601887],"genre_scores_gemma":[0.9948182,0.00006441516,0.004818624,0.00002000849,0.00005284394,0.00002379666,2.798619e-7,0.000004324027,0.0001975185],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9375837,"threshold_uncertainty_score":0.3134236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01849431547794771,"score_gpt":0.2278304370027861,"score_spread":0.2093361215248384,"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."}}