Topology Learning Aided False Data Injection Attack without Prior Topology Information

Higgins, Martin; Zhang, Jiawei; Zhang, Ning; Teng, Fei

doi:10.1109/pesgm46819.2021.9638211

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…Indeed, the authors have explored these type of attacks previously in Refs. [34][35][36] in case studies where FDI attackers alter system measurements to spoof the transmission-level state estimation processes. However, while FDI style attacks on transmission level infrastructure have received significant research attention, limited research has examined the impact of these attacks on distribution-level systems such as smart meter load profiles.…”

Section: Attacks Against Metering Infrastructurementioning

confidence: 99%

Detecting smart meter false data attacks using hierarchical feature clustering and incentive weighted anomaly detection

Higgins

Stephen

Wallom

2023

IET Cyber-Phy Sys Theory & Ap

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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 financial fraud via false data injection (FDI) style attacks. The authors consider attacks which aim to alter the consumer load profile to exploit intraday price dips. The authors examine an anomaly detection protocol for cyber‐attacks that seek to leverage spot prices for financial gain. In this way the authors outline a methodology for detecting attacks on industrial load smart meters. The authors first create a feature clustering model of the underlying business, segregated by business type. The authors then use these clusters to create an incentive‐weighted anomaly detection protocol for false data attacks against load profiles. This clustering‐based methodology incorporates both the load profile and spot pricing considerations for the detection of injected load profiles. To reduce false positives, the authors model incentive‐based detection, which includes knowledge of spot prices, into the anomaly tracking, enabling the methodology to account for changes in the load profile which are unlikely to be attacks.

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Section: Attacks Against Metering Infrastructurementioning

confidence: 99%

Detecting smart meter false data attacks using hierarchical feature clustering and incentive weighted anomaly detection

Higgins

Stephen

Wallom

2023

IET Cyber-Phy Sys Theory & Ap

Self Cite

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“…Voltage V and phase angle θ are the system states, while active power P and reactive power Q are the parameters measured in each physical node. g ij and b ij denote to the conductance and susceptance between node i and j respectively [27]. In the paper [28], Gu et al constructed an attack of the system by adjusting the state variables.…”

Section: Constrained Fdi Modellingmentioning

confidence: 99%

Virtual‐physical power flow method for cyber‐physical power system contingency and vulnerability assessment

Qiu,

Zhang,

Zhou

et al. 2023

IET Smart Grid

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Traditional power systems have evolved into cyber‐physical power systems (CPPS) with the integration of information and communication technologies. CPPS can be considered as a typical hierarchical control system that can be divided into two parts: the power grid and the communication network. CPPS will face new vulnerabilities which can have network contingencies and cascading consequences. To address this challenge, a virtual‐physical power flow (VPPF) method is proposed for the vulnerability assessment of CPPS. The proposed method contains dual power flows, one is to simulate a virtual power flow from decision‐making units, and the other is to simulate a physical power flow. In addition, a novel hierarchical control model is proposed that includes four layers of CPPS: the physical layer, the secondary device layer, the regional control layer, and the national control layer. The model is based on IEEE test cases using data and structures provided by MATPOWER. Denial‐of‐service (DoS) and false data injection (FDI) are simulated as two major cyber‐attacks in CPPS. A novel vulnerability index is proposed that consists of system voltage, network latency, and node betweenness as three key indicators. This is a comprehensive and adaptive index because it encompasses both cyber and physical system characteristics and can be applied to several types of cyber‐attacks. The results of the vulnerability assessment are compared in national and regional control structures of CPPS to evaluate the vulnerability of cyber‐physical nodes.

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Cyber–physical risk assessment for false data injection attacks considering moving target defences

Higgins

Teng

et al. 2022

Int. J. Inf. Secur.

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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. Existing research considers the FDI attack in the context of the ability to change a measurement in a static system only. However, successful attacks will require first intrusion into a system followed by construction of an attack vector that can bypass bad data detection to cause a consequence (such as overloading). Furthermore, the recent development of moving target defences (MTD) introduces dynamically changing system topology, which is beyond the capability of existing research to assess. In this way, we develop a full service framework for FDI risk assessment. The framework considers both the costs of system intrusion via a weighted graph assessment in combination with a physical, line overload-based vulnerability assessment under the existence of MTD. We present our simulations on a IEEE 14-bus system with an overlain RTU network to model the true risk of intrusion. The cyber model considers multiple methods of entry for the FDI attack including meter intrusion, RTU intrusion and combined style attacks. Post-intrusion, our physical reinforcement model analyses the required level of topology divergence to protect against a branch overload from an optimised attack vector. The combined cyber and physical index is used to represent the system vulnerability against FDIA.

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Topology Learning Aided False Data Injection Attack without Prior Topology Information

Cited by 7 publications

References 23 publications

Detecting smart meter false data attacks using hierarchical feature clustering and incentive weighted anomaly detection

Detecting smart meter false data attacks using hierarchical feature clustering and incentive weighted anomaly detection

Virtual‐physical power flow method for cyber‐physical power system contingency and vulnerability assessment

Cyber–physical risk assessment for false data injection attacks considering moving target defences

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