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.

翻译：区块链技术经历了快速发展，并已在医疗保健、金融和能源等多个领域得到广泛应用。然而，区块链平台仍然容易受到各种网络攻击，特别是那些旨在利用交易和智能合约（SCs）窃取数字资产或破坏系统完整性的攻击。为解决这一问题，我们提出了一种新颖且有效的框架，用于检测区块链系统中的网络攻击。我们的框架首先采用一个预处理工具，利用自然语言处理（NLP）技术将区块链交易的关键特征转换为图像表示。随后，这些图像通过基于视觉的分析方法进行分析，使用视觉Transformer（ViT）——计算机视觉领域的一项最新进展，以其捕捉复杂模式和语义关系的卓越能力而闻名。通过将基于NLP的预处理与基于视觉的学习相结合，我们的框架能够检测多种攻击类型。在基准数据集上的实验评估表明，我们的方法在区块链交易和智能合约的网络攻击检测方面，无论是准确率（达到99.5%）还是鲁棒性，均显著优于现有的最先进方法。