A Secure Blockchain-Based Offline Payment System with Fraud Window Analysis

Abstract

The rapid growth of digital payment systems has increased the demand for secure and reliable payment mechanisms capable of operating in environments with intermittent or unavailable Internet connectivity. Conventional blockchain-based payment systems typically require continuous network access for transaction validation and settlement, which limits their applicability in rural areas, disaster-affected regions, and other low-connectivity environments. This research presents a Secure Blockchain-Based Offline Payment System with Explicit Fraud Window Analysis, which allows transactions to be created and stored locally during periods of network unavailability and synchronized with the Ethereum blockchain once connectivity is restored. The proposed system integrates an offline wallet, digitally signed transaction processing, local transaction verification, blockchain-based settlement, and a fraud-detection mechanism. Its central contribution is the explicit measurement of the fraud window, the time interval between offline transaction creation and blockchain synchronization, which is analysed alongside pending-transaction activity and trust-related parameters to estimate transaction risk. The system was implemented using Solidity, Ethereum, Hardhat, Ethers.js, JavaScript, Node.js, and MetaMask. The resulting prototype demonstrates the feasibility of secure offline transaction processing that preserves blockchain-based final settlement while providing measurable visibility into the security risk introduced by delayed synchronization.

Introduction

The proposed Secure Blockchain-Based Offline Payment System with Fraud Window Analysis enables digital payments during temporary Internet unavailability. Transactions are created and digitally signed while offline, stored locally, and synchronized with the Ethereum blockchain once connectivity is restored. Smart contracts then validate and settle the transactions.

The key contribution is Fraud Window Analysis, which measures the time between transaction generation and blockchain synchronization:

FW=Tsync−TgenFW = T_{sync} - T_{gen}

This time interval is analysed together with security indicators such as duplicate transaction IDs, nonce reuse, rapid transaction bursts, abnormal payment amounts, unusual receiver diversity, and spending concentration. A rule-based risk engine categorizes transactions as APPROVE, FLAG, or BLOCK.

The system architecture consists of an offline wallet, transaction engine, local storage, synchronization/fraud-analysis backend, and Ethereum smart-contract layer. EIP-712 is used for structured digital signatures, while nonces and transaction IDs provide additional replay and duplication protection. The smart contract performs sender verification, nonce and expiry checks, transaction uniqueness validation, balance/escrow verification, and settlement recording.

A prototype was implemented using MetaMask, Hardhat, Solidity, Ethers.js, Node.js/Express, JSON-based storage, and an HTML/JavaScript dashboard. Functional testing demonstrated successful offline transaction creation, local storage, digital signing, synchronization, fraud checks, and blockchain settlement.

The illustrative fraud-window analysis showed delays ranging from 12 to 75 seconds. A delay above the prototype's 60-second analytical window receives additional scrutiny, but a long delay alone is not considered proof of fraud. Suspicious behaviour is determined by combining temporal information with other transaction indicators.

Overall, the work demonstrates that offline authorization and later blockchain settlement can be integrated into a single payment architecture, while explicitly measuring the period of exposure between transaction creation and on-chain reconciliation. The main limitation is that the prototype is not production-ready: it uses browser localStorage and a JSON ledger, a local Hardhat blockchain rather than real network conditions, and a rule-based fraud engine rather than a machine-learning model. Future work could therefore focus on hardware-backed security/TEE support, stronger persistent storage, real blockchain testing, and advanced fraud-detection models.

Conclusion

This paper presented a Secure Blockchain-Based Offline Payment System with Fraud Window Analysis for payment scenarios in which network connectivity is temporarily unavailable. The architecture separates payment authorization from blockchain settlement: transactions are created, signed and stored locally offline, then synchronized with a backend and submitted to an Ethereum smart contract for validation and settlement once connectivity returns. Its main analytical contribution is the Fraud Window, FW = Tsync ? Tgen, which is combined with transaction-level indicators, duplicate IDs, nonce reuse, bursts, abnormal amounts, receiver diversity and spending concentration, rather than used alone as proof of fraud.

References

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Copyright

Copyright © 2026 Kuda Sravani Sandhya Sree. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.