Machine Learning-Based Vulnerability Detection in Ethereum Smart Contracts via EVM Bytecode Feature Engineering

Solovev, Sergei

doi:10.6084/m9.figshare.31429971

Machine Learning-Based Vulnerability Detection in Ethereum Smart Contracts via EVM Bytecode Feature Engineering

Sergei Solovev

2026-02-26 · Preprint, Figshare · DOI: 10.6084/m9.figshare.31429971

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Abstract

XGBoost + Optuna on 117,091 Slither-labelled Ethereum contracts achieves F1=0.948 using 65 hand-crafted bytecode features in 15 SWC-mapped semantic categories. Binary classification: recall 0.950, MCC 0.832, PR-AUC 0.990.

Keywords: smart contract security; EVM bytecode; XGBoost; Optuna; Slither labels; binary classification; SWC categories