The rapid convergence of artificial intelligence and blockchain technologies has increased the demand for efficient and accurate methods to detect fraudulent behavior in smart contract–driven systems. Smart contracts automate digital transactions in decentralized environments, yet they remain vulnerable to fraud while operating under strict computational and scalability constraints. In this study, we propose an evolutionary-guided CNN compression framework tailored for Convolutional Neural Networks (CNNs) aimed at improving fraud detection in smart contract analysis while significantly reducing model complexity. The proposed approach uses evolutionary optimization to guide structured model compression, enabling the removal of redundant parameters without compromising predictive performance. Experimental evaluations demonstrate up to a 50% reduction in model parameters while maintaining 97.8–97.9% classification accuracy, making the resulting models suitable for deployment in resource-constrained environments. By combining evolutionary optimization with CNN-based fraud detection, this work provides an efficient and interpretable solution for smart contract analysis, supporting scalable and practical deployment in blockchain-related security applications.
Albanyan et al. (Sat,) studied this question.