Blockchain-Anchored Detection and Recovery for Resilient Reinforcement Learning-Based Cyber-Physical Systems

Published in: Proceedings of the 7th National HBCU Blockchain, FinTech, and AI Conference
Conference: 7th National HBCU Blockchain, FinTech, and AI Conference
Conference Location: Nashville, Tennessee
Date of Conference: November 8–10, 2026
Publisher: National Center for the Study of Blockchain and FinTech, Morgan State University

Published: 2026
Publication Type: Full Paper
Pages: 1-15
Paper ID: 1

Author(s)

Imran Jahid Khan
Department of Computer Science, Tuskegee University

Maliha Hossain
Department of Computer Science, Tuskegee University

Mohammad Rahman
Department of Computer Science, Tuskegee University

Fan Wu
Department of Computer Science, Tuskegee University

Abstract

Reinforcement learning (RL) controllers in cyber-physical systems (CPS) assume that sensor observations describe the true plant state, yet replay, sensor-drift, and false-data-injection (FDI) attacks can corrupt the observation stream at inference time, and detection alone does not tell the controller what to use after an alarm. This study evaluates a proof-of-concept defense that combines a SHA-256 hash-linked ledger, which provides exact-match replay detection and a tamper-evident recovery anchor, with a Long Short-Term Memory autoencoder (LSTM-AE) for audit alarms and a fixed Proximal Policy Optimization (PPO) controller. Ten independently trained policies per environment are evaluated in paired episodes on CartPole-v1 and LunarLander-v3. Content-hash matching detects every injected bitwise replay with zero false matches in 379,396 clean steps. However, perfect detection does not guarantee safe recovery: exact-gated ledger hold raises LunarLander replay return from 178.4 to 264.6 (paired mean +86.2, 95% CI [64.8, 107.6]) but lowers CartPole from 411.1 to 376.2 (−34.9, not significant after Holm adjustment). A trigger-by-anchor factorial ablation shows that local and ledger anchors are control-equivalent under a fixed trigger, while LSTM-AE-gated hold is harmful in every tested cell. A 4,800-episode sensitivity study across three severity levels per attack shows that the recovery effect scales with severity in opposite directions: +5.8 to +225.0 on LunarLander and −4.6 to −60.3 on CartPole. The ledger contributes exact-replay lookup, provenance, and tamper evidence rather than an automatic control advantage; safe general recovery further requires dynamics-aware substitution and distributed, physical validation.

Keywords: cyber-physical systems, reinforcement learning, blockchain, anomaly detection, replay attacks, resilient control

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