Autoencoder-Driven Framework for Zero-Day Vulnerability Detection in Ethereum Contracts

Abstract

The security of smart contracts is critical to the integrity of decentralized systems. Unlike traditional software, their immutability makes them particularly susceptible to zero-day vulnerabilities unseen flaws that can lead to catastrophic financial losses once exploited. Traditional detection methods, which rely on predefined attack patterns, are fundamentally incapable of addressing such unknown threats. This paper introduces a novel deep learning framework designed to proactively detect both known and previously unobserved zero-day vulnerabilities in Ethereum smart contracts. The approach employs a dual-path architecture that combines CodeBERT for deep semantic feature extraction with two parallel detection modules: a Graph Neural Network (GNN) for classifying known threats and a dedicated Autoencoder for unsupervised anomaly detection. This dual-path system leverages the strengths of both supervised and unsupervised learning. The GNN effectively classifies known attack vectors, while the Autoencoder identifies latent anomalies by flagging contracts with high reconstruction errors, a key indicator of unseen vulnerabilities. The framework was trained and validated on a balanced subset of the Malicious Smart Contract Detection dataset. The GNN demonstrated a high classification accuracy for known vulnerabilities, and the Autoencoder successfully identified anomalous contracts that deviated from learned patterns. This dual-pronged methodology represents a significant step forward in bolstering blockchain security by providing a robust, data-driven defense against the evolving landscape of smart contract vulnerabilities.