Predictive Reputation-Based Consensus: Hardening Decoupled Machine Learning Against Telemetry Spoofing in Enterprise Blockchains

Abstract

Permissioned blockchain networks require high transaction throughput and absolute data privacy, yet traditional consensus protocols rely on reactive heuristics that leave enterprise ledgers highly vulnerable to covert Byzantine threats such as sleeper decay and cartel collusion. While decoupled machine learning sidecars utilizing an optimized eXtreme Gradient Boosting (XGBoost) architecture can proactively detect these pre-crime footprints without introducing fatal consensus serialization risks, they remain fundamentally susceptible to sophisticated telemetry spoofing. When adversaries feed the classifier fabricated metrics, baseline accuracy collapses catastrophically from 97.25 percent to 33.33 percent. To address this critical vulnerability, this research proposes the Telemetry Attestation and Resilience Integration Module (TARIM), a multi-layer defense framework that systematically detects and corrects telemetry manipulation. By incorporating hardware-anchored telemetry via Intel SGX, cross-peer behavioral triangulation, and statistical integrity verification, the system evaluates node intent asynchronously without blocking ledger operations. Evaluated against a 6-class Byzantine attack taxonomy across 12,000 continuous epochs, TARIM recovered global classification accuracy to 99.08 percent under active attack. Furthermore, adversarial retraining successfully resolved baseline weaknesses, achieving a near-perfect recall rate against sustained decay. Crucially, the 20-feature TARIM pipeline required merely 4.895 milliseconds of inference latency, consuming a negligible 0.245 percent of the 2000-millisecond Raft timeout threshold. Ultimately, this research proves that predictive artificial intelligence can preemptively secure permissioned ledgers, and it demonstrates that these intelligence layers can be structurally hardened against data poisoning without degrading enterprise transaction speeds.