A Blockchain-Enabled Neutrosophic Framework for Secure, Verifiable, and Adaptive Configuration Management Effectiveness and Computer Network Telemetry

This paper presents a novel decision-making framework for secure and uncertainty-aware management of computer networks by applying Double Bounded Rough Neutrosophic Sets (DBRNS). The proposed method addresses the inherent ambiguity, incompleteness, and inconsistency found in network telemetry and configuration change data. Unlike conventional network management systems that rely on fixed thresholds or binary rules, the DBRNS-based approach classifies operational states into three categories: low, medium, and high risk or impact based on neutrosophic logic. This classification explicitly models truth, indeterminacy, and falsity degrees for each state, while rough set approximations delineate regions of certainty and possibility in multi-dimensional network feature space. The decision outputs guide automated or human-supervised actions such as immediate application of safe changes, staged rollouts, or quarantine for further review. To ensure tamper-proof auditability, the framework integrates a permissioned blockchain to store Merkle-root summaries of telemetry batches and signed configuration transactions, enabling efficient off-chain data storage with on chain verifiable proofs. A complete mathematical formulation is provided, along with a fully calculated enterprise-scale case study demonstrating the framework’s robustness under noisy, incomplete data, its security guarantees against common attack vectors, and its low-latency performance under Practical Byzantine Fault Tolerance (PBFT) consensus.