Projects and Tools

Modernization of Critical Infrastructure

We work in two areas in the modernization of critical infrastructure

• Research into the cybersecurity of industrial control systems
• Modernization of legacy hardware, instrumentation, and protocols.

Students in our lab have the opportunity to implement modern control system elements including safety instrumented systems, distributed I/O, advanced process control, and soft sensors and secure them using best practices.

The laboratory features three DeltaV distributed control systems provided through an Education Agreement with Emerson. We use them as Test, Development, and Production systems.

We now track over 400 tags, connected using a wide variety of different industrial protocols, including: HART, Wireless HART, Profibus DP, Profibus PA, Modbus TCP, EtherNet/IP, DeviceNet, OPC DA, and OPC UA.

We deal extensively with OPC UA programming, using Open62541 and GOPCUA.

We work with NIST 800-82R2 and ISA/IEC 62443 cybersecurity frameworks.

Students in our lab also get the chance to learn:

• Mapping potential attacks to the MITRE ICS ATT&CK framework
• Various command and control architectures
• Emerging network standards such as Ethernet-TSN
• Server virualization and implications of IEC 62443 or NERC CIP
• Containerized workloads and Kubernetes (k3s in particular)
• Setting up firewalls, VLANs, and ACLs
• Packet analysis with Wireshark
• Network-based and host intrusion detection (Security Onion, Suricata, Snort, Zeek)
• Rules writing for Suricata
• Protocol parsing in Wireshark and Suricata

Analytics and Data Science

Dark data – data which is locked away in disparate propietrary systems – has hindered informed decision making for decades. Our students learn to work with data in modern, usable formats and transform it into actionable intelligence.

Our cloud historian stack is based on:

• Custom developed OPC UA plugin for Telegraf, available on our Github.
• InfluxDB for time series data storage
• Grafana for visualization and dashboarding

In Process Analytics, we teach students to process this data using the Python language and Jupyter notebooks. We use NumPy, scikit-learn, and pandas for analysis. For plotting we use Plotly, Seaborn, and Matplotlib.