1 citations · 2 across the 7 of their papers we have counts for
7 papers
Incentive-weighted Anomaly Detection for False Data Injection Attacks Against Smart Meter Load Profiles
Martin Higgins, Bruce Stephen, David Wallom
Spot pricing is often suggested as a method of increasing demand-side flexibility in electrical power load. However, few works have considered the vulnerability of spot pricing to…
Spatial-Temporal Anomaly Detection for Sensor Attacks in Autonomous Vehicles
Martin Higgins, Devki Jha, David Wallom
Time-of-flight (ToF) distance measurement devices such as ultrasonics, LiDAR and radar are widely used in autonomous vehicles for environmental perception, navigation and assisted…
Blending Data and Physics Against False Data Injection Attack: An Event-Triggered Moving Target Defence Approach
Wangkun Xu, Martin Higgins, Jianhong Wang +2
Fast and accurate detection of cyberattacks is a key element for a cyber-resilient power system. Recently, data-driven detectors and physics-based Moving Target Defences (MTD) have…
Cyber-Physical Risk Assessment for False Data Injection Attacks Considering Moving Target Defences
Martin Higgins, Wangkun Xu, Fei Teng +1
In this paper, we examine the factors that influence the success of false data injection (FDI) attacks in the context of both cyber and physical styles of reinforcement. Many works…
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…
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…
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…