{"id":"W4400112242","doi":"10.1109/tdsc.2024.3420712","title":"PerfSPEC: Performance Profiling-Based Proactive Security Policy Enforcement for Containers","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Information and Cyber Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Profiling (computer programming); Enforcement; Computer security; Security policy; Business; Computer science; Political science; Law; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004027906,0.0002470428,0.0002046365,0.0003268853,0.0005937699,0.0004353437,0.0002940325,0.0001040725,0.00001410131],"category_scores_gemma":[0.000005816623,0.0002268066,0.0001289393,0.0004672371,0.0000453945,0.0007347327,0.000006995309,0.0003727447,0.00001583981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001541576,"about_ca_system_score_gemma":0.0003530884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005368404,"about_ca_topic_score_gemma":0.00001656747,"domain_scores_codex":[0.9984792,0.00003956129,0.000335477,0.0004416523,0.0002733204,0.0004308198],"domain_scores_gemma":[0.9992506,0.0001634753,0.00006506065,0.0002584566,0.000131245,0.0001311544],"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":[0.0002994836,0.0004844437,0.00004513447,0.002164231,0.0004219898,0.0000317347,0.02894188,0.1279004,0.000635497,0.3825991,0.0005318595,0.4559443],"study_design_scores_gemma":[0.0005944573,0.0002787041,0.000006388257,0.0001016544,0.0000191979,0.00002729469,0.000223399,0.9527899,0.04057087,0.0004819076,0.004635118,0.000271082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03669082,0.00009440105,0.9588528,0.0006195686,0.0006547322,0.0006922184,0.00002787939,0.0004139191,0.00195363],"genre_scores_gemma":[0.9917765,0.00002045251,0.007360522,0.0005496953,0.0001096301,0.00005916562,0.000008944645,0.00001648196,0.00009858645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9550857,"threshold_uncertainty_score":0.9248906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01223280080943709,"score_gpt":0.2579279129151216,"score_spread":0.2456951121056845,"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."}}