International Research Journal of Education and Technology

Fraudulent activities within Bitcoin transactions present a pressing concern for the integrity of financial systems, particularly in the context of smart cities where digital currencies are increasingly prevalent. In response to this challenge, we propose a pioneering framework for fraud detection that harnesses the power of machine learning, specifically leveraging the XGBoost algorithm. Our framework integrates the robust security features of blockchain technology to combat illegal transactions such as money laundering, dark web transactions, and ransomware payments.While blockchain technology offers a decentralized and secure ledger for recording transactions, it falls short in detecting fraudulent patterns within legitimate transactions. To address this limitation, our solution introduces a novel approach by incorporating ensemble stacking models. These models combine the strengths of various machine learning algorithms to enhance the accuracy and reliability of fraud identification.

By integrating machine learning with blockchain technology, our framework aims to provide a comprehensive solution for detecting and preventing fraudulent activities in Bitcoin transactions within the dynamic landscape of smart cities. Our system is designed to proactively identify anomalies and deviations from normal transaction behavior, enabling timely intervention to prevent fraudulent activities.This proposed framework represents a significant advancement in bolstering the security of smart cities' financial ecosystems. By capitalizing on the transparency and immutability of blockchain technology and harnessing the sophisticated pattern recognition capabilities of machine learning, our solution offers a comprehensive and adaptive approach to combating the evolving landscape of fraudulent activities within Bitcoin transactions.

KEYWORDS:  Fraud Detection, Bitcoin, Smart Cities, Machine Learning, XGBoost, Blockchain Technology, Ensemble Stacking Models, Anomaly Detection.