Cryptographically Secured Machine Learning for Resilient Multi-Tier Supply Chains

Abstract

As we move further into a more digital supply chain, we have seen tremendous improvements in terms of efficiency, but we have also seen a rise in the risks that data vulnerabilities pose, particularly for intermediary supply chain nodes. As a result, traditional security measures have proven inadequate in protecting critical data that flows through these intermediary supply chain nodes, making them more susceptible to possible security breaches and tampering. In this paper, we introduce a novel solution that utilizes cryptography and Artificial Intelligence (AI)-powered predictive modeling for the security of supply chain data. Unlike other solutions that only protect endpoint data, our solution provides a more comprehensive security solution that extends cryptography for intermediary supply chain data, making it more secure and protected from possible breaches and tampering. To further improve our solution, we have utilized AI models, namely, XGBoost, Random Forest, and LightGBM, for predictive modeling. To prevent target leakage, features arithmetically derived from the recovery-duration target were excluded from the predictor set prior to training. The results indicate that, once the leakage-affected features are removed and hyperparameters are properly tuned, all five models achieve modest but genuine predictive accuracy, with R2 values in the range of 0.542–0.552. The best-performing model on this leakage-audited feature set is XGBoost, with an R2 value of 0.5525 and a test RMSE of 38.16 days, closely followed by Linear Regression and Ridge Regression (RMSE = 38.22 days, R2 = 0.5512), with the practical difference between the two being small (0.06 days RMSE) despite being statistically consistent across ten random splits. This demonstrates the solution’s effectiveness in reducing security risks while maintaining realistic, leakage-free predictive accuracy.