Civil Engineering & Construction: Faculty Publications

A Digital Twin Framework for Cyber-Physical System Detection in Water Treatment Infrastructure

Document Type

Conference Proceeding

Publication Date

4-20-2026

Publication Title

Conference Proceedings - IEEE SOUTHEASTCON

DOI

10.1109/SoutheastCon63549.2026.11475926

Abstract

Water treatment plants increasingly rely on networked sensing and supervisory control, yet many facilities operate with legacy infrastructure and limited cyber defenses, leaving critical processes vulnerable to cyber-physical attacks. While legal and regulatory frameworks recognize the societal impact of disrupting clean water access, recent incidents demonstrate that adversaries continue to target water utilities. This paper presents a digital twin (DT)-driven detection framework that couples a discrete-event simulation (DES) model of a water treatment process with a long short-term memory (LSTM) classifier for attack detection and categorization. A conventional treatment train is modeled to generate event-level process data under normal operation and under targeted attacks on chemical dosing pumps. Using five classes, the proposed DT+LSTM approach achieves over 92 % accuracy, precision, recall, and F1-score in detecting and classifying simulated attacks. These results demonstrate the feasibility of DES-based DTs as cyber-physical systems for monitoring water treatment operations and motivate further development toward higher-fidelity plant models and broader attack coverage.

Comments

Georgia Southern University faculty member, Lewis S. Rowles and Rami J. Haddad co-authored, "A Digital Twin Framework for Cyber-Physical System Detection in Water Treatment Infrastructure."

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