The integration of smart sensors and actuators in industrial environments has expanded the cyber-physical attack surface, making it increasingly difficult to distinguish anomalies caused by cyberattacks from those due to mechanical or electrical faults. This challenge is exacerbated by stealthy, multi-stage attacks leveraging Living off the Land (LOTL) techniques, which often evade conventional anomaly detection or intrusion detection systems (IDS).
This study presents a Digital Twin-based testbed for safe, repeatable simulation of multi-stage cyber-physical attacks targeting Cyber-Physical Systems (CPS) and Industrial Control Systems (ICS). We propose a two-level decision fusion method that aggregates and aligns anomalies across network, process, and host domains in synchronized 1-minute intervals. The first-level fusion improves OT-layer detection by applying confidence-aware decision logic to outputs combined from (a) a supervised deep learning model (LSTM-FCN) for process anomalies, (b) an unsupervised model (Isolation Forest) for OPC UA network anomalies, and (c) process alarm signals. The second-level fusion integrates these results with host-based anomalies, computed through point-based scoring of Wazuh alerts, to provide comprehensive IT/OT situational awareness. Experimental results demonstrate improved detection of stealthy, multi-stage APT attack behaviours. Additionally, Large Language Models (LLM) provide summarization of the integrated IT/OT anomaly logs into human-readable insights, enhancing interpretability and supporting cyber threat hunting.