A Review of False Data Injection Attacks Against Modern Power Systems

Liang, Gaoqi; Zhao, Junhua; Luo, Fengji; Weller, Steven R.; Zhang, Dong

doi:10.1109/tsg.2015.2495133

Cited by 799 publications

(314 citation statements)

References 36 publications

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“…Subsequently, the network manager's goal will be the detection of these misreports and suppressing their potential catastrophic impacts on the network. A similar problem is studied in the context of smart grid networks with a different system model and failure assumptions, e.g., [33], [34], [35]. However, the model proposed in these works does not explicitly focus on the interdependency between the demand and the supply.…”

Section: Remarkmentioning

confidence: 99%

Prevention and Mitigation of Catastrophic Failures in Demand-Supply Interdependent Networks

Hosseinalipour

Mao

Eun

et al. 2020

IEEE Trans. Netw. Sci. Eng.

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We propose a generic system model for a special category of interdependent networks, demand-supply networks, in which the demand and the supply nodes are associated with heterogeneous loads and resources, respectively. Our model sheds a light on a unique cascading failure mechanism induced by resource/load fluctuations, which in turn opens the door to conducting stress analysis on interdependent networks. Compared to the existing literature mainly concerned with the node connectivity, we focus on developing effective resource allocation methods to prevent these cascading failures from happening and to mitigate/confine them upon occurrence in the network. To prevent cascading failures, we identify some dangerous stress mechanisms, based on which we quantify the robustness of the network in terms of the resource configuration scheme. Afterward, we identify the optimal resource configuration under two resource/load fluctuations scenarios: uniform and proportional fluctuations. We further investigate the optimal resource configuration problem considering heterogeneous resource sharing costs among the nodes. To mitigate/confine ongoing cascading failures, we propose two network adaptations mechanisms: intentional failure and resource re-adjustment, based on which we propose an algorithm to mitigate an ongoing cascading failure while reinforcing the surviving network with a high robustness to avoid further failures.

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Section: Remarkmentioning

confidence: 99%

Prevention and Mitigation of Catastrophic Failures in Demand-Supply Interdependent Networks

Hosseinalipour

Mao

Eun

et al. 2020

IEEE Trans. Netw. Sci. Eng.

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“…To address the above issues, two schemes have been widely studied to detect FDI attacks [4], [14]: One way is to strategically protect a number of secure basic measurements. Kim et al [12] propose a greedy algorithm to select a subset of base measurements and the placement of secure phasor measurement units.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Detection of False Data Injection Attack in Smart Grid using Deep Learning

Niu

Sun

et al. 2019

2019 IEEE Power &Amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)

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Modern advances in sensor, computing, and communication technologies enable various smart grid applications. The heavy dependence on communication technology has highlighted the vulnerability of the electricity grid to false data injection (FDI) attacks that can bypass bad data detection mechanisms. Existing mitigation in the power system either focus on redundant measurements or protect a set of basic measurements. These methods make specific assumptions about FDI attacks, which are often restrictive and inadequate to deal with modern cyber threats. In the proposed approach, a deep learning based framework is used to detect injected data measurement. Our time-series anomaly detector adopts a Convolutional Neural Network (CNN) and a Long Short Term Memory (LSTM) network. To effectively estimate system variables, our approach observes both data measurements and network level features to jointly learn system states. The proposed system is tested on IEEE 39-bus system. Experimental analysis shows that the deep learning algorithm can identify anomalies which cannot be detected by traditional state estimation bad data detection.

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“…The 2 -test or as commonly called, residue-based test [11], is considered to be the conventional detection solution [12][13][14] typically used in CPSs. The 2 -test utilizes a normalized version of the power of the residuals based on the steady-state innovation covariance.…”

Section: Introductionmentioning

confidence: 99%

Attack Detection/Isolation via a Secure Multisensor Fusion Framework for Cyberphysical Systems

2018

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Motivated by rapid growth of cyberphysical systems (CPSs) and the necessity to provide secure state estimates against potential data injection attacks in their application domains, the paper proposes a secure and innovative attack detection and isolation fusion framework. The proposed multisensor fusion framework provides secure state estimates by using ideas from interactive multiple models (IMM) combined with a novel fuzzy-based attack detection/isolation mechanism. The IMM filter is used to adjust the system's uncertainty adaptively via model probabilities by using a hybrid state model consisting of two behaviour modes, one corresponding to the ideal scenario and one associated with the attack behaviour mode. The state chi-square test is then incorporated through the proposed fuzzy-based fusion framework to detect and isolate potential data injection attacks. In other words, the validation probability of each sensor is calculated based on the value of the chi-square test. Finally, by incorporation of the validation probability of each sensor, the weights of its associated subsystem are computed. To be concrete, an integrated navigation system is simulated with three types of attacks ranging from a constant bias attack to a non-Gaussian stochastic attack to evaluate the proposed attack detection and isolation fusion framework.

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A Review of False Data Injection Attacks Against Modern Power Systems

Cited by 799 publications

References 36 publications

Prevention and Mitigation of Catastrophic Failures in Demand-Supply Interdependent Networks

Prevention and Mitigation of Catastrophic Failures in Demand-Supply Interdependent Networks

Dynamic Detection of False Data Injection Attack in Smart Grid using Deep Learning

Attack Detection/Isolation via a Secure Multisensor Fusion Framework for Cyberphysical Systems

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