{"id":"W2136222807","doi":"10.1109/ares.2008.75","title":"Boosting Markov Reward Models for Probabilistic Security Evaluation by Characterizing Behaviors of Attacker and Defender","year":2008,"lang":"en","type":"article","venue":"","topic":"Information and Cyber Security","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Dependability; Probabilistic logic; Computer security; TRACE (psycholinguistics); Markov decision process; Context (archaeology); Markov chain; Markov process; Artificial intelligence; Machine learning; 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":[0.0006452976,0.00009848439,0.0001335057,0.00005066359,0.0001519963,0.00004088497,0.0001797396,0.00005572868,0.00001370995],"category_scores_gemma":[0.00006398038,0.00009144425,0.00004160701,0.0001077777,0.00004424003,0.001072348,0.00009089169,0.00006232801,0.000001632791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003460583,"about_ca_system_score_gemma":0.00006818207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002214391,"about_ca_topic_score_gemma":0.000005504965,"domain_scores_codex":[0.9989656,0.00004397895,0.0003147976,0.0002018737,0.0003013608,0.0001723728],"domain_scores_gemma":[0.9992338,0.00005918409,0.0001414681,0.0002192159,0.0002884292,0.00005793848],"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":[0.0002563945,0.001622352,0.02569812,0.001827652,0.0001952465,0.000007818139,0.208743,0.001198653,0.01329201,0.5456032,0.03962834,0.1619272],"study_design_scores_gemma":[0.0007316984,0.00007120911,0.003371995,0.00002375978,0.00002026352,0.00002304004,0.000111923,0.9844883,0.002882388,0.007630017,0.0004139837,0.0002314027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7760909,0.00004997091,0.2199708,0.0002138556,0.00009625349,0.000635052,0.00001320015,0.00007333075,0.002856612],"genre_scores_gemma":[0.9858739,0.000008880241,0.01373491,0.0002372518,0.00001046331,0.0000560493,0.00002371649,0.000004571701,0.00005030468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9832897,"threshold_uncertainty_score":0.3728988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04248478956729863,"score_gpt":0.2749106381132718,"score_spread":0.2324258485459731,"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."}}