چکیده مقاله
As technology continues to transform the landscape of financial transactions, the need for effective fraud detection systems has become increasingly critical This paper explores the synergistic integration of machine learning ML and blockchain technology to enhance the efficacy of fraud detection mechanisms Using a comprehensive dataset of Ethereum transactions, we propose an innovative framework that leverages advanced ML algorithms—specifically XGBoost and Random Forest—combined with the secure and decentralized features of the blockchain’s immutable ledger Our findings show that this integration significantly improves both detection accuracy and operational efficiency, with XGBoost emerging as the most effective algorithm in this context The inherent immutability of blockchain technology bolsters the pattern recognition capabilities of ML, resulting in a robust solution for real time fraud detection This research not only underscores the potential of combining these two cutting edge technologies but also paves the way for future advancements in secure financial transactions
کلیدواژهها
نویسندگان
Zahra Davoodian
Dept of Industrial Engineering Amirkabir University of Technology Tehran, Iran
Ehsan Hajizadeh
Assistant Professor, Dept of Industrial Engineering Amirkabir University of Technology Tehran, Iran
Soheil Vasigh Mehr
Dept of Industrial Engineering Amirkabir University of Technology Tehran, Iran
شیوه ارجاع
Davoodian, Zahra and Hajizadeh, Ehsan and Vasigh Mehr, Soheil,1403,Enhancing Fraud Detection in Financial Transactions: A Machine Learning and Blockchain Integration Framework,The 10th International Conference on Industrial and Systems Engineering,Mashhad
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