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

2025-12-11

AI Summary

To address the challenge of detecting diverse network attacks targeting blockchain smart contracts and transactions, this paper proposes a cross-modal anomaly detection method. First, transaction sequences are preprocessed using natural language processing (NLP) techniques and encoded into two-dimensional images; subsequently, a Vision Transformer (ViT) is employed to model visual representations, enabling cross-modal mapping from semantic features to spatial structures. The approach unifies modeling of transaction temporal dynamics, call-graph relationships, and contract logic, supporting end-to-end detection of multiple attack types—including reentrancy, integer overflow, and flash loan exploits. Evaluated on mainstream benchmark datasets, the method achieves 99.5% accuracy, substantially outperforming existing state-of-the-art approaches. It demonstrates high precision, strong robustness against adversarial perturbations, and enhanced model interpretability. This work establishes a novel vision-driven paradigm for blockchain security analysis.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Security and Privacy: Cryptocurrency and smart contracts
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Detects cyberattacks in blockchain transactions and smart contracts
• Transforms transaction features into images using NLP preprocessing
• Uses Vision Transformers to analyze images for attack patterns

Innovation

• NLP transforms transactions into image representations
• Vision Transformers analyze images for attack patterns
• Combines NLP and vision learning for high accuracy detection