The objective of the project is to create a comprehensive solution, comprising both hardware and software, to monitor, identify, and mitigate attacks on the Industrial Control System/Operational Technology (ICS/OT) within factories and industrial organizations. To achieve this objective, we perform research on attack risks across many layers, ranging from the user application layer to the physical device layer of the ICS/OT system. Consequently, we developed an OT Security Appliance that facilitates early detection of attacks from both internal and external sources, is simple to deploy, and exhibits strong compatibility with the device levels inside the infrastructure. The centralized management application, OT Security Dashboard, will be built to enable the monitoring and management of security devices, offering an in-depth view of the system’s information security posture. This project seeks to use advanced machine learning models to improve the analysis of access flows and the detection of attacks on ICS/OT systems.
Related Activities
Publications
- ECODI: A Novel Evolutionary Coreset Distillation with LLM-Assisted Fitness Evaluation for Encrypted Network Traffics , 2025
- Zero Trust: Deep Learning and NLP for HTTP Anomaly Detection in IDS , 2025