{"id":"W2982581462","doi":"10.48550/arxiv.1910.14169","title":"Secure Logging with Security against Adaptive Crash Attack","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Adversary; Computer security; Scheme (mathematics); Insider; Crash; Computer security model; File system; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001223003,0.0004566152,0.0004284363,0.0001710546,0.0001222575,0.000270152,0.001985996,0.0002866543,0.000009143771],"category_scores_gemma":[0.000006998184,0.0004486937,0.0002143845,0.0005005107,0.0001916953,0.00082047,0.002829801,0.00080422,0.0002628339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002011539,"about_ca_system_score_gemma":0.0003178981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004565363,"about_ca_topic_score_gemma":0.00007589388,"domain_scores_codex":[0.997715,0.00007120003,0.0001680208,0.001376569,0.0001713343,0.0004978502],"domain_scores_gemma":[0.9978198,0.00006657232,0.0002656566,0.001416393,0.0002185603,0.0002130334],"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":[0.00009211999,0.0002638911,0.00754945,0.0002256961,0.0004147745,0.001702516,0.00150433,0.4857598,0.000003754166,0.4968035,0.003734334,0.001945802],"study_design_scores_gemma":[0.001188953,0.0003009503,0.001364191,0.0005449041,0.0001134112,0.00002107284,0.0003797386,0.8788328,0.0001431262,0.1097196,0.005597108,0.001794152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5539268,0.0000486265,0.3609871,0.0001061838,0.0007348776,0.0004329168,0.00005778021,0.0004653485,0.08324046],"genre_scores_gemma":[0.9964026,0.00004571557,0.0008129344,0.0002695046,0.00007067864,6.488719e-7,0.00003050478,0.0000257454,0.002341655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4424759,"threshold_uncertainty_score":0.9997965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04846037471739188,"score_gpt":0.1692540080754501,"score_spread":0.1207936333580582,"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."}}