A Generative Model Based Honeypot for Industrial OPC UA Communication

TL;DR

This paper presents a novel generative machine learning-based honeypot for industrial OPC UA communication, capable of mimicking dynamic industrial processes to detect cyber-attacks in OT systems.

Contribution

It introduces a proof-of-concept honeypot using LSTM networks to replicate industrial OPC UA communication and publishes a related dataset for cyclic industrial processes.

Findings

The honeypot can feasibly replicate cyclic industrial processes.

Generated trajectories are stable and plausible in the short-term.

The implementation is resource-efficient on constrained hardware.

Abstract

Industrial Operational Technology (OT) systems are increasingly targeted by cyber-attacks due to their integration with Information Technology (IT) systems in the Industry 4.0 era. Besides intrusion detection systems, honeypots can effectively detect these attacks. However, creating realistic honeypots for brownfield systems is particularly challenging. This paper introduces a generative model-based honeypot designed to mimic industrial OPC UA communication. Utilizing a Long ShortTerm Memory (LSTM) network, the honeypot learns the characteristics of a highly dynamic mechatronic system from recorded state space trajectories. Our contributions are twofold: first, we present a proof-of concept for a honeypot based on generative machine-learning models, and second, we publish a dataset for a cyclic industrial process. The results demonstrate that a generative model-based honeypot can feasibly replicate a cyclic industrial process via OPC UA communication. In the short-term, the generative model indicates a stable and plausible trajectory generation, while deviations occur over extended periods. The proposed honeypot implementation operates efficiently on constrained hardware, requiring low computational resources. Future work will focus on improving model accuracy, interaction capabilities, and extending the dataset for broader applications.