Bridging the PLC Binary Analysis Gap: A Cross-Compiler Dataset and Neural Framework for Industrial Control Systems

TL;DR

This paper introduces PLC-BEAD, a large dataset of PLC binaries with source code and labels, and presents PLCEmbed, a transformer-based model achieving high accuracy in binary analysis tasks for industrial control systems.

Contribution

The paper provides the first comprehensive dataset pairing PLC binaries with source code and labels, along with a transformer-based framework for binary analysis in industrial control systems.

Findings

PLCEmbed achieves 93% accuracy in compiler provenance identification.

PLCEmbed achieves 42% accuracy in functionality classification.

The dataset enables reproducible research in PLC security and reverse engineering.

Abstract

Industrial Control Systems (ICS) rely heavily on Programmable Logic Controllers (PLCs) to manage critical infrastructure, yet analyzing PLC executables remains challenging due to diverse proprietary compilers and limited access to source code. To bridge this gap, we introduce PLC-BEAD, a comprehensive dataset containing 2431 compiled binaries from 700+ PLC programs across four major industrial compilers (CoDeSys, GEB, OpenPLC-V2, OpenPLC-V3). This novel dataset uniquely pairs each binary with its original Structured Text source code and standardized functionality labels, enabling both binary-level and source-level analysis. We demonstrate the dataset's utility through PLCEmbed, a transformer-based framework for binary code analysis that achieves 93\% accuracy in compiler provenance identification and 42\% accuracy in fine-grained functionality classification across 22 industrial control categories. Through comprehensive ablation studies, we analyze how compiler optimization levels, code patterns, and class distributions influence model performance. We provide detailed documentation of the dataset creation process, labeling taxonomy, and benchmark protocols to ensure reproducibility. Both PLC-BEAD and PLCEmbed are released as open-source resources to foster research in PLC security, reverse engineering, and ICS forensics, establishing new baselines for data-driven approaches to industrial cybersecurity.