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A deep learning-based solution for securing the power grid against load altering threats by IoT-enabled devices
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Jahangir, Hamidreza, Lakshminarayana, Subhash, Maple, Carsten and Epiphaniou, Gregory (2023) A deep learning-based solution for securing the power grid against load altering threats by IoT-enabled devices. IEEE Internet of Things Journal, 10 (12). pp. 10687-10697. doi:10.1109/JIOT.2023.3240289 ISSN 2327-4662.
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WRAP-deep-learning-based-solution-securing-power-grid-against-load-altering-threats-IoT-enabled-devices-2023.pdf - Accepted Version - Requires a PDF viewer. Download (1394Kb) | Preview |
Official URL: http://doi.org/10.1109/JIOT.2023.3240289
Abstract
The growing integration of high-wattage Internet-of-Things (IoT)-enabled electrical appliances at the consumer end has created a new attack surface that an adversary can exploit to disrupt power grid operations. Specifically, dynamic load-altering attacks (D-LAAs), accomplished by an abrupt or strategic manipulation of a large number of consumer appliances in a botnet-type attack, have been recognized as major threats that can potentially destabilize power grid control loops. This paper introduces a novel approach based a multi-output network (two-dimensional convolutional neural networks classifier and reconstruction decoder)-called “2DR-CNN”-to detect and localize D-LAAs with high resolution. To achieve this, we leverage the frequency and phase angle data of the generator buses monitored by phasor measurement units (PMUs) installed in the power grid. To verify the effectiveness of the proposed method, simulations are conducted on IEEE 14-and 39-bus systems. The performance of the 2DR-CNN method is compared against several benchmark machine learning-based approaches. The results confirm that the proposed method outperforms other techniques in detection and localizing D-LAAs with high resolution in a number of practical scenarios, including PMU measurement noises and missing measurements.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
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Library of Congress Subject Headings (LCSH): | Electric power distribution -- Security measures, Neural networks (Computer science), Deep learning (Machine learning), Internet of things -- Security measures, Cyberterrorism -- Prevention | ||||||||
Journal or Publication Title: | IEEE Internet of Things Journal | ||||||||
Publisher: | IEEE | ||||||||
ISSN: | 2327-4662 | ||||||||
Official Date: | June 2023 | ||||||||
Dates: |
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Volume: | 10 | ||||||||
Number: | 12 | ||||||||
Page Range: | pp. 10687-10697 | ||||||||
DOI: | 10.1109/JIOT.2023.3240289 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | © 2023 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 7 March 2023 | ||||||||
Date of first compliant Open Access: | 9 March 2023 | ||||||||
RIOXX Funder/Project Grant: |
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