Data Sheet 5_GENESIS-SHIELD: an interpretable ensemble for anomaly detection in CRISPR genomic-workflow security.csv

These are distinct from the biological risks of editing itself; our focus is the security of the digital record. Existing single-component detectors capture only one facet of these threats.</p>Methods<p>We present GENESIS-SHIELD, a multi-component ensemble whose novelty lies in the integration of established techniques for CRISPR-workflow security rather than in any single new algorithm.

It combines four components—a Hierarchical Blockchain Merkle Tree with Bloom filters and a train-registry integrity check, an adaptive cross-layer entropy/KL analyzer, an interpretable decision-tree ethics rule system, and a structural temporal-consistency detector—whose weights are set by Bayesian optimization of a recall-oriented (F₂) validation objective. We benchmark against classic (Isolation Forest, One-Class SVM, LOF) and modern (XGBoost, autoencoder, and Deep-SVDD) baselines trained on identical features, and report a leave-one-component-out ablation, bootstrap confidence intervals, and isotonic calibration.</p>Results<p>On a synthetic benchmark of 50,000 records (5% anomalies, eight categories), the ensemble attains AUC-ROC 0.982 (95% CI 0.973–0.990) and AUC-PR 0.841, with the proposed weighted detector reaching recall 0.978 at precision 0.500 (F₁ 0.661).