Vision-Based Learning for Cyberattack Detection in Blockchain Smart Contracts and Transactions

• URL: http://arxiv.org/abs/2512.11272v1
• Date: Fri, 12 Dec 2025 04:28:06 GMT
• Title: Vision-Based Learning for Cyberattack Detection in Blockchain Smart Contracts and Transactions
• Authors: Do Hai Son, Le Vu Hieu, Tran Viet Khoa, Yibeltal F. Alem, Hoang Trong Minh, Tran Thi Thuy Quynh, Nguyen Viet Ha, Nguyen Linh Trung,
• Abstract summary: We propose a novel and effective framework for detecting cyberattacks within blockchain systems.<n>Our framework begins with a preprocessing tool that uses Natural Language Processing (NLP) techniques to transform key features of blockchain transactions into image representations.<n>These images are then analyzed through vision-based analysis using Vision Transformers (ViT), a recent advancement in computer vision known for its superior ability to capture complex patterns and semantic relationships.
• Score: 1.6301630538569725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Blockchain technology has experienced rapid growth and has been widely adopted across various sectors, including healthcare, finance, and energy. However, blockchain platforms remain vulnerable to a broad range of cyberattacks, particularly those aimed at exploiting transactions and smart contracts (SCs) to steal digital assets or compromise system integrity. To address this issue, we propose a novel and effective framework for detecting cyberattacks within blockchain systems. Our framework begins with a preprocessing tool that uses Natural Language Processing (NLP) techniques to transform key features of blockchain transactions into image representations. These images are then analyzed through vision-based analysis using Vision Transformers (ViT), a recent advancement in computer vision known for its superior ability to capture complex patterns and semantic relationships. By integrating NLP-based preprocessing with vision-based learning, our framework can detect a wide variety of attack types. Experimental evaluations on benchmark datasets demonstrate that our approach significantly outperforms existing state-of-the-art methods in terms of both accuracy (achieving 99.5%) and robustness in cyberattack detection for blockchain transactions and SCs.

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