Data Analytics for Cyber Event Detection, Management, and Recovery

CREDC is developing data-analytic techniques tuned to discover evidence for the presence of malicious actors in the EDS system. This involves state-of-the-art analysis of physical and cyber system behavior and advanced cyber-physical simulation and optimization models. CREDC is also leveraging the rapidly expanding body of knowledge about data analytics in large datasets in order to further develop and apply that technology to the special structure of EDS and data sets gathered from both the physical and cyber systems.

Related Research Activities

Anomaly Detection for Securing Communications in Advanced Metering Infrastructure
Data Sharing for Energy Delivery Systems
Evaluating Effectiveness of an Embedded System Endpoint Security Technology on EDS OT: Defeating the Hackers of IIoT Devices
Federated Simulation for Development of Improved Incident Detection and Management
Online, Context-aware, Intelligent Anomaly Detection, Causality and Consequence Analysis, and Response Suggestion for Supervisory Control and Data Acquisition (SCADA) Systems in Energy Distribution Systems
Proactive Response Strategy for Energy Delivery Systems
Supporting Security with Advanced Multimodal Grid Data Analytics
Towards Attack Resilient Data Analytics for Power Grid Operations

Related Publications

A Learning Scheme for Microgrid Reconnection
An Emulation Environment for Prototyping PMU Data Errors
An Interactive, Extensible Environment for Power System Simulation on the PMU Time Frame with a Cyber Security Application
Anomaly Detection Using Optimally-Placed micro-PMU Sensors in Distribution Grids
Automated Anomaly Detection in Distribution Grids Using Micro-PMU Measurements
Data Injection Attacks in Randomized Gossiping
Detecting Manipulation of Power Distribution System Control Devices
EDMAND: Edge-Based Multi-Level Anomaly Detection for SCADA Networks
F-DETA: A Framework for Detecting Electricity Theft Attacks in Smart Grids
Learning Process Behavioral Baselines for Anomaly Detection
Learning Schemes for Power System Planning and Control
Load Oscillating Smart Meter Attack
Low-Resolution Fault Localization Using Phasor Measurement Units with Community Detection
Micro Synchrophasor-Based Intrusion Detection in Automated Distribution Systems: Towards Critical Infrastructure Security
Online Thevenin Parameter Tracking Using Synchrophasor Data
Peer-to-peer Detection of DoS Attacks on City-Scale IoT Mesh Networks
Real-time Detection of Malicious PMU Data