A Generative Model Based Honeypot for Industrial OPC UA Communication
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 honeyp...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2410.21574 |