Analysis of Load-Altering Attacks Against Power Grids: A Rare-Event Sampling Approach

Goodridge, Maldon Patrice; Lakshminarayana, Subhash; Christopher, Few,

doi:10.1109/pmaps53380.2022.9810602

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Our studies suggest that LAA magnitudes in the range of hundreds of megawatts can severely impact the power grid's safety, potentially leading to cascading failures [7]. As grid operators make increased use of DSR to help balance supply and demand on the grid, LAAs of such a large magnitude are in the realm of possibility in the near future.…”

Section: Attack Impact Analysismentioning

confidence: 90%

Data‐driven detection and identification of IoT‐enabled load‐altering attacks in power grids

2022

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Advances in edge computing are powering the development and deployment of Internet of Things (IoT) systems to provide advanced services and resource efficiency. However, large‐scale IoT‐based load‐altering attacks (LAAs) can seriously impact power grid operations, such as destabilising the grid's control loops. Timely detection and identification of any compromised nodes are essential to minimise the adverse effects of these attacks on power grid operations. In this work, two data‐driven algorithms are proposed to detect and identify compromised nodes and the attack parameters of the LAAs. The first method, based on the Sparse Identification of Nonlinear Dynamics approach, adopts a sparse regression framework to identify attack parameters that best describe the observed dynamics. The second method, based on physics‐informed neural networks, employs neural networks to infer the attack parameters from the measurements. Both algorithms are presented utilising edge computing for deployment over decentralised architectures. Extensive simulations are performed on IEEE 6‐, 14‐, and 39‐bus systems to verify the effectiveness of the proposed methods. Numerical results confirm that the proposed algorithms outperform existing approaches, such as those based on unscented Kalman filter, support vector machines, and neural networks (NN), and effectively detect and identify locations of attack in a timely manner.

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Section: Attack Impact Analysismentioning

confidence: 90%

Data‐driven detection and identification of IoT‐enabled load‐altering attacks in power grids

2022

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“…• An analysis of the impact of MaDIoT attacks on the simplified model of the European power system: the PST-16 benchmark model. To the authors' knowledge, this model is used for the study of MaDIoT attacks for the first time in this paper since previous works have mainly used American system models [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Confronting the Threat: Analysis of the Impact of MaDIoT Attacks in Two Power System Models

Rodríguez-Pérez,

Matanza Domingo,

Sigrist

et al. 2023

Energies

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The increasing penetration of Internet of Things (IoT) devices at the consumer level of power systems also increases the surface of attack for the so-called Manipulation of Demand through IoT (MaDIoT) attacks. This paper provides a comparison of the impact that MaDIoT attacks could have on power systems with different characteristics, such as the IEEE 39-Bus (New England) and the PST-16 system (simplified European model), by assuming that the attacker does not have advanced knowledge of the grid. The results for the IEEE 39-Bus system expand and complement the results obtained by previous work. The simulation results show that these systems present significant differences between them with respect to the success probability of an attack, being in general much higher for the IEEE 39-Bus system. In the PST-16 system, the required number of bots to obtain a certain success probability varies depending on the area attacked. However, a high probability of success does not necessarily mean a high impact on the system. This paper shows that the response to the high-impact MaDIoT attacks of the two models considered is very different as the initial impact of the attack on the system also differs, mainly affecting rotor angles in the PST-16 system, and the frequency in the IEEE 39-Bus.

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Analysis of Cascading Failures Due to Dynamic Load-Altering Attacks

Goodridge,

Zocca,

Lakshminarayana

2023

2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

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Analysis of Load-Altering Attacks Against Power Grids: A Rare-Event Sampling Approach

Abstract: How to cite:Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 4 publications

References 16 publications

Data‐driven detection and identification of IoT‐enabled load‐altering attacks in power grids

Data‐driven detection and identification of IoT‐enabled load‐altering attacks in power grids

Confronting the Threat: Analysis of the Impact of MaDIoT Attacks in Two Power System Models

Analysis of Cascading Failures Due to Dynamic Load-Altering Attacks

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