AI-Augmented Blockchain Fork Detection and Recovery Protocols
DOI:
https://doi.org/10.63345/Keywords:
Blockchain Forks, Reorg Detection, Anomaly Detection, Graph Neural Networks, Changepoint Analysis, Fork-Choice Rules, Recovery Protocols, Mempool Hygiene, P2P Relay OptimizationAbstract
Forks—temporary or permanent divergences in a blockchain’s ledger—arise from benign network asynchrony, configuration heterogeneity, or adversarial behavior such as selfish mining and eclipse/routing attacks. Although modern fork-choice rules (e.g., longest-chain/most-work, GHOST variants) and finality gadgets reduce the practical impact of short reorgs, detection remains largely reactive and recovery is often ad hoc at the protocol or operational layer. This paper proposes an AI-augmented framework for (i) early fork risk prediction from peer-to-peer (P2P) telemetry and block-header streams, (ii) real-time fork detection using multi-view anomaly models, and (iii) policy-driven recovery that coordinates mempool hygiene, relay routing, and safe rollback/finality choices.
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