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@article{191562,
        author = {shiva},
        title = {Decentralized Fraud Detection Using Blockchain and Adaptive Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {7149-7153},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191562},
        abstract = {With the rapid increase in digital financial transactions, the threat of fraud has significantly intensified, challenging the reliability of traditional machine learning (ML)-based fraud detection systems. These existing systems often rely on centralized databases that are vulnerable to tampering by insiders, leading to compromised ML models and inaccurate fraud predictions. To address these concerns, this project proposes a novel framework that integrates Blockchain technology with Machine Learning to provide a tamper-proof, privacy-preserving, and adaptive fraud detection system. Blockchain ensures data integrity and security by storing model weights in decentralized, immutable blocks verified through cryptographic hash functions. Smart contracts developed using Solidity enable secure storage and retrieval of ML model weights on the Ethereum blockchain.},
        keywords = {Blockchain, Machine Learning, Fraud Detection, Privacy Preservation, Smart Contracts, Ethereal Blockchain, Decentralized System, Tamper-Proof Data Storage},
        month = {January},
        }