1 citations · 1 across the 2 of their papers we have counts for
3 papers
eess.SY2021
Topology Learning Aided False Data Injection Attack without Prior Topology Information
Martin Higgins, Jiawei Zhang, Ning Zhang +1
False Data Injection (FDI) attacks against powersystem state estimation are a growing concern for operators.Previously, most works on FDI attacks have been performedunder the assum…
eess.SY2020★ 1 cited
Enhanced Cyber-Physical Security Using Attack-resistant Cyber Nodes and Event-triggered Moving Target Defence
Martin Higgins, Keith Mayes, Fei Teng
This paper outlines a cyber-physical authentication strategy to protect power system infrastructure against false data injection (FDI) attacks. We demonstrate that it is feasible t…
eess.SY2020
Stealthy MTD Against Unsupervised Learning-based Blind FDI Attacks in Power Systems
Martin Higgins, Fei Teng, Thomas Parisini
This paper examines how moving target defences (MTD) implemented in power systems can be countered by unsupervised learning-based false data injection (FDI) attack and how MTD can…