machine-learning

Cybersecurity via Inverter-Grid Automatic Reconfiguration (CIGAR)

This project is performing R&D to enable distribution grids to adapt to resist a cyber-attack by (1) developing adaptive control algorithms for DER, voltage regulation, and protection systems; (2) analyze new attack scenarios and develop associated defensive strategies. It is funded by DOE OE’s CEDS program and is co-led by Sean Peisert and Daniel Arnold.

Integrated Multi Scale Machine Learning for the Power Grid

The goal of this project is to create advanced, distributed data analytics capability to provide visibility and controllability to distribution grid operators. It is funded by the DOE Grid Modernization Initiative. The LBNL portion of this effort is led by Sean Peisert.

Detecting Distributed Denial of Service Attacks on Wide-Area Networks

This project develops techniques for detecting DDoS attacks and disambiguating them from large-scale science flows. It is funded by the DOE iJC3 Cyber R&D program and is led by Sean Peisert.

Toward a Hardware/Software Co-Design Framework for Ensuring the Integrity of Exascale Scientific Data

This project takes a broad look at several aspects of security and scientific integrity issues in HPC systems. It is funded by DOE ASCR and is led by Sean Peisert.

Power Grid Threat Detection and Response with Data Analytics

The goal of this project is to develop technologies and methodologies to protect the nation’s power grid from advanced cyber and all-hazard threats. This will be done through the collection of disparate data and the use of advanced analytics to detect threats and response to them. It is funded by DOE OE’s CEDS program via the Grid Modernization Initiative and is co-led by Sean Peisert.

Inferring Computing Activity Using Physical Sensors

This project uses power data to monitor the use of computing systems, including supercomputers and large computing centers. It is led by Sean Peisert.

Secure and Private Acquisition, Storage, and Analysis of Medical Sensor Data

This project is developing a system-based workflow to securely acquire wireless data from mechanical ventilators in critical care environments, and leverage scalable web-based analytic platforms to advance data analytics and visualization of issues surrounding patients with respiratory failure.

NetSage - an open privacy-aware network measurement, analysis, and visualization service

NetSage is a network measurement, analysis and visualization service funded by the National Science Foundation and is designed to address the needs of today’s international networks. This project is co-led by Sean Peisert at LBNL.

Cyber Security of Power Distribution Systems by Detecting Differences Between Real-time Micro-Synchrophasor Measurements and Cyber-Reported SCADA

This project is using micro-PMU measurements and SCADA commands to develop a system to detect cyberattacks against the power distribution grid. It is funded by DOE OE’s CEDS program and is led by Sean Peisert.

The Hive Mind: Applying a Distributed Security Sensor Network to GENI.

This project sought to define and prototype a security layer using a method of intrusion detection based on mobile agents and swarm intelligence. The project was funded by NSF’s CISE Directorate, and was led by Sean Peisert.