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

Abstract

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. 

DOI: 10.5281/zenodo.16887764