activity
20192021
collaborators

5 papers

eess.SY2021

A Verifiable Framework for Cyber-Physical Attacks and Countermeasures in a Resilient Electric Power Grid

Zhigang Chu, Andrea Pinceti, Ramin Kaviani +10

In this paper, we investigate the feasibility and physical consequences of cyber attacks against energy management systems (EMS). Within this framework, we have designed a complete…

eess.SY2020

Reliability Makes It Difficult for False Data Injection Attacks to Cause Physical Consequences

Zhigang Chu, Jiazi Zhang, Oliver Kosut +1

This paper demonstrates that false data injection (FDI) attacks are extremely limited in their ability to cause physical consequences on reliable power systems operating with…

eess.SY2020

Detecting Load Redistribution Attacks via Support Vector Models

Zhigang Chu, Oliver Kosut, Lalitha Sankar

A machine learning-based detection framework is proposed to detect a class of cyber-attacks that redistribute loads by modifying measurements. The detection framework consists of a…

eess.SY2019

A Nonlinear Regression Method for Composite Protection Modeling of Induction Motor Loads

Soumya Kundu, Zhigang Chu, Yuan Liu +6

Protection equipment is used to prevent damage to induction motor loads by isolating them from power systems in the event of severe faults. Modeling the response of induction motor…

eess.SY2019

Vulnerability Assessment of N-1 Reliable Power Systems to False Data Injection Attacks

Zhigang Chu, Jiazi Zhang, Oliver Kosut +1

This paper studies the vulnerability of large-scale power systems to false data injection (FDI) attacks through their physical consequences. Prior work has shown that an attacker-d…