{"id":"W4407564937","doi":"10.3390/e27020202","title":"SAluMC: Thwarting Side-Channel Attacks via Random Number Injection in RISC-V","year":2025,"lang":"en","type":"article","venue":"Entropy","topic":"Security and Verification in Computing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; New York Institute of Technology","funders":"","keywords":"Computer science; Side channel attack; Embedded system; Cache; Microarchitecture; Software; Timing attack; Channel (broadcasting); Computer hardware; Computer network; Cryptography; Computer security; 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":[0.0004843437,0.0001061453,0.0001509423,0.0001216593,0.0001660294,0.0001378001,0.0004013007,0.0000688866,0.00002846408],"category_scores_gemma":[0.0001421406,0.0001111277,0.00006238499,0.0006587792,0.00002309658,0.0002842031,0.0001653619,0.0002122312,0.0001878951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008520991,"about_ca_system_score_gemma":0.00004751291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001391259,"about_ca_topic_score_gemma":0.0000188068,"domain_scores_codex":[0.9987864,0.0001371975,0.0003248873,0.0003409616,0.0001585901,0.0002518921],"domain_scores_gemma":[0.9992492,0.0002258462,0.00009831449,0.0003360833,0.00005379411,0.00003673698],"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":[0.0002975503,0.0008861492,0.1023511,0.0002139129,0.000168822,0.00007273174,0.01734322,0.04187439,0.009376214,0.6729843,0.02408148,0.1303501],"study_design_scores_gemma":[0.002476006,0.00002911118,0.01257598,0.0001155941,0.000007088753,0.00001883344,0.000147618,0.9237367,0.006951851,0.04731856,0.006347216,0.000275423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06704826,0.00008589437,0.9236017,0.001030933,0.001289999,0.0001541167,2.994135e-7,0.0001926235,0.006596178],"genre_scores_gemma":[0.9929877,0.000006021852,0.006058686,0.0004925888,0.000126156,0.00001676797,0.000001580029,0.00000498079,0.0003055065],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9259394,"threshold_uncertainty_score":0.4531654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01149871167667506,"score_gpt":0.276479255521842,"score_spread":0.2649805438451669,"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."}}