Verifier-Faithful, Enforcing In-Kernel Machine Learning for Host Intrusion Detection: generated results data

Generated results data supporting the article Verifier-Faithful, Enforcing In-Kernel Machine Learning for Host Intrusion Detection. Contains the canonical aggregator output, the per-experiment result files behind every figure and table, the content-hashed calibration, and the recorded execution environment. Covered are: the four public system-call corpora (ADFA-LD, UNM Live lpr, AWSCTD, NGIDS-DS) over 30 leakage-free folds; in-kernel fidelity and BPF-LSM enforcement measurements taken at the live security hook; adversarial mimicry and sequence-evasion budgets; per-system-call and application-level runtime overhead; a freshly captured native virtual-machine benchmark with real multi-attack enforcement; and a behavioural benchmark that holds program identity constant, with its live in-kernel detect-and-deny confirmation. Every results file declares STATUS: validated. Confidence intervals are 5000-resample percentile bootstrap; pairwise comparisons carry Holm step-down correction. Source code is not part of this deposit. Raw third-party corpora are not redistributed; each is obtainable from the original host cited in the article's Data availability statement.