{"id":"W2139065819","doi":"10.1109/compsac.2009.54","title":"A Framework for Cost Sensitive Assessment of Intrusion Response Selection","year":2009,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Intrusion detection system; Computer science; Intrusion; Plug-in; Selection (genetic algorithm); Process (computing); Response time; Set (abstract data type); Risk analysis (engineering); Reliability engineering; Data mining; Engineering; Machine learning; Operating system","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":[0.01312815,0.003071371,0.002312461,0.005025645,0.0008307991,0.005525561,0.003838782,0.002012811,0.003072041],"category_scores_gemma":[0.03027256,0.001267215,0.001526908,0.002946656,0.001851917,0.004805717,0.002845788,0.003383603,0.0007034376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003244597,"about_ca_system_score_gemma":0.002659455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004033978,"about_ca_topic_score_gemma":0.0022106,"domain_scores_codex":[0.9878036,0.004722856,0.0007700424,0.001298981,0.004841026,0.0005635647],"domain_scores_gemma":[0.9844449,0.008763594,0.001617116,0.001732905,0.003040372,0.0004011986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001524308,0.000212878,0.002029022,0.0002322203,0.0002002017,0.000250738,0.0002244737,0.6377813,0.004312683,0.2305875,0.003459101,0.1205574],"study_design_scores_gemma":[0.00001368906,0.00006328172,0.000319109,0.00002574348,0.00003053973,0.000105892,0.00002968181,0.9478745,0.001530604,0.04728043,0.002687258,0.00003918041],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002156001,0.0001283415,0.9954449,0.0001708227,0.00002731545,0.0001149953,0.00006373069,0.0004885444,0.001405383],"genre_scores_gemma":[0.2060852,0.0003732885,0.7898951,0.0001448241,0.0001566486,0.0006040216,0.0002340337,0.0002748361,0.00223209],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01312815,"threshold_uncertainty_score":0.0694291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01813776125732773,"score_gpt":0.3150176911548915,"score_spread":0.2968799298975638,"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."}}