Mohamed Abd El-Fattah Mohamed Hamed Awad
Professor
Optimized Framework for Detecting Suspicious Accounts in the Ethereum Blockchain Networ
• 2025
Publication Information
Authors
Noha E El-Attar, Marwa H Salama, Mohamed Abdelfattah, Sanaa Taha
Keywords
Not Available
Journal
Not Available
Publisher
Not Available
Volume
Not Available
Issue
Not Available
Pages
Not Available
publication.type
International
Paper Link
Not Available
Supplementary Materials
Not Available
Abstract
Detecting, tracking, and preventing cryptocurrency money laundering within blockchain
systems is a major challenge for governments worldwide. This paper presents an anomaly
detection model based on blockchain technology and machine learning to identify cryptocurrency money-laundering accounts within Ethereum blockchain networks. The proposed model employs Particle Swarm Optimization (PSO) to select optimal feature subsets.
Additionally, three machine learning algorithms—XGBoost, Isolation Forest (IF), and Support Vector Machine (SVM)—are employed to detect suspicious accounts. A Genetic
Algorithm (GA) is further applied to determine the optimal hyperparameters for each
machine learning model. The evaluations demonstrate the superiority of the XGBoost
algorithm over SVM and IF, particularly when enhanced with GA. It achieved accuracy, precision, recall, and F1-score values of 0.98, 0.97, 0.98, and 0.97, respectively. After applying
GA, XGBoost’s performance metrics improved to 0.99 across all categories
systems is a major challenge for governments worldwide. This paper presents an anomaly
detection model based on blockchain technology and machine learning to identify cryptocurrency money-laundering accounts within Ethereum blockchain networks. The proposed model employs Particle Swarm Optimization (PSO) to select optimal feature subsets.
Additionally, three machine learning algorithms—XGBoost, Isolation Forest (IF), and Support Vector Machine (SVM)—are employed to detect suspicious accounts. A Genetic
Algorithm (GA) is further applied to determine the optimal hyperparameters for each
machine learning model. The evaluations demonstrate the superiority of the XGBoost
algorithm over SVM and IF, particularly when enhanced with GA. It achieved accuracy, precision, recall, and F1-score values of 0.98, 0.97, 0.98, and 0.97, respectively. After applying
GA, XGBoost’s performance metrics improved to 0.99 across all categories