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

TL;DR

This paper introduces a novel vision-based framework combining NLP preprocessing and Vision Transformers to detect cyberattacks in blockchain transactions and smart contracts, significantly improving accuracy and robustness.

Contribution

It presents a new approach that transforms blockchain transaction features into images and applies vision transformers for effective attack detection.

Findings

Achieves 99.5% detection accuracy

Outperforms existing methods in robustness

Effective across various attack types

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.