{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006506531,0.00008133776,0.0001254365,0.0001117629,0.0001338159,0.00004584869,0.0001515987,0.00009806422,0.00001833246],"category_scores_gemma":[0.0001291575,0.00007282742,0.00006450288,0.0004571499,0.00001530274,0.0002591229,0.00004177839,0.0001311681,0.000002976115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006899132,"about_ca_system_score_gemma":0.00006210784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006546089,"about_ca_topic_score_gemma":0.000005263344,"domain_scores_codex":[0.9991134,0.0001381546,0.0001915066,0.0002285841,0.000173299,0.0001550601],"domain_scores_gemma":[0.9990965,0.000377194,0.0001009069,0.0001922644,0.0001864655,0.00004663798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005727998,0.0001644828,0.00003866814,0.000006510402,0.0000121896,0.000001414394,0.0004339619,0.0006499016,0.03138733,0.7230496,0.001298574,0.2423846],"study_design_scores_gemma":[0.0004625281,0.002124491,0.01918797,0.00006878404,0.000008109619,0.00002210487,0.0000413659,0.7698135,0.1023242,0.1004233,0.005324105,0.0001995352],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06335975,0.000004810997,0.9336254,0.001746622,0.0002540951,0.0003383041,9.210446e-7,0.0001090429,0.0005610795],"genre_scores_gemma":[0.7068564,0.000007714654,0.2924612,0.0005410873,0.00006288628,0.00000823034,7.243061e-7,0.000002208735,0.00005960971],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7691636,"threshold_uncertainty_score":0.2969816,"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."}}