Recent active projects

GMLC 1.4.9 - Integrated Multi Scale Data Analytics and Machine Learning

Cohesive view of the future distribution grid and its building interface, an interactive environment where there are consumer benefits and motivations to leverage customer behind-the-meter assets. Large spatial footprint of the distribution grid and diverse locations of its assets make observability, monitoring and diagnosis of abnormal (faults) and even planned (demand response or DER dispatch) events challenging tasks for the existing descriptive analytics field, but great for Machine Learning.

• Python framework created to gather data from Modbus devices through OpenFMB adapters and apply analytics to that data and then publish the data via NATS. Architecture to develop OpenFMB apps created.

Deep-Cyberia

DEEP-CYBERIA (Deep Cyber-Physical System Interrogation and Analysis), a broadly applicable, novel capability to deepen understanding and strengthen the resilience of cyber-physical assets. We develop a network discovery capability that will be integrated into the ORNL Digital Twin (DT) framework established in FY18 to enhance discovering, monitoring, and diagnosing the identity of CPS components.

CyberForce competition – Multi-lab ICS cybersecurity competition

• Developed challenges for students to solve during the competition.
• Coordinated ORNL red team activities with other red teams distributed across seven US National Labs.

Presented talk at BSides Knoxville 2019 titled: Find out what happens when 70 universities and 1000 volunteers participate in a cyber security competition.

Presenters: Jeff Nichols, Chris Craig and myself

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EDS Verify

Automated, remote verification of the integrity of the firmware of energy delivery system (EDS) devices, focusing on volatile memory. Provide a tool to verify the integrity of firmware used in EDS devices, without taking the equipment out of service.

Digital Twin for Cyber-physical system security

Here we developed Digital Twins for several energy grid components and used advanced analytics to determine where there were anomalies in the system.

see my publications