Increasing the resiliency of power systems in presence of GPS spoofing attacks: A data‐driven deep‐learning algorithm
Abstract The growing use of wireless technologies in power systems has raised concerns about cybersecurity, particularly regarding GPS spoofing attacks (GSAs). These attacks manipulate GPS data, leading to modifications in the phase angle of phasor measurement units (PMUs). In this paper, a Deep‐lea...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-10-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12929 |
| _version_ | 1797650790645497856 |
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| author | Mohammad Sabouri Sara Siamak Maryam Dehghani Mohsen Mohammadi Mohammad Hasan Asemani Mohammad Reza Hesamzadeh Vedran Peric |
| author_facet | Mohammad Sabouri Sara Siamak Maryam Dehghani Mohsen Mohammadi Mohammad Hasan Asemani Mohammad Reza Hesamzadeh Vedran Peric |
| author_sort | Mohammad Sabouri |
| collection | DOAJ |
| description | Abstract The growing use of wireless technologies in power systems has raised concerns about cybersecurity, particularly regarding GPS spoofing attacks (GSAs). These attacks manipulate GPS data, leading to modifications in the phase angle of phasor measurement units (PMUs). In this paper, a Deep‐learning GPS‐Spoofing Counteraction (DLGSC) algorithm is proposed, utilizing PMU data for GSA detection and PMU data correction. The algorithm incorporates a recurrent neural network (RNN) and a set of long short‐term memory (LSTM) units separately, for signal correction after attack detection. Unlike existing methods that struggle with simultaneous attacks or they are static methods, DLGSC tackles these challenges by leveraging deep learning techniques. By selecting appropriate features for GSA detection, DLGSC achieves accurate results. The algorithm is evaluated on standard IEEE 14‐bus and IEEE 39‐bus power systems, and its performance is compared to statistical, dynamic, and Deep Learning (DL) methods in the literature. Additionally, an experimental setup is designed to validate the algorithm in a laboratory environment. Results demonstrate the easy‐implementable DLGSC algorithm's satisfactory real‐time performance in various scenarios, such as load variations and noise, achieving over 98% accuracy. Notably, DLGSC is cable of detecting multiple GSAs on different PMUs. |
| first_indexed | 2024-03-11T16:06:43Z |
| format | Article |
| id | doaj.art-db351b0288da4375999ad877584259b6 |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-03-11T16:06:43Z |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-db351b0288da4375999ad877584259b62023-10-25T03:36:23ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952023-10-0117204525454010.1049/gtd2.12929Increasing the resiliency of power systems in presence of GPS spoofing attacks: A data‐driven deep‐learning algorithmMohammad Sabouri0Sara Siamak1Maryam Dehghani2Mohsen Mohammadi3Mohammad Hasan Asemani4Mohammad Reza Hesamzadeh5Vedran Peric6School of Electrical and Computer Engineering Shiraz University ShirazIranSchool of Electrical and Computer Engineering Shiraz University ShirazIranSchool of Electrical and Computer Engineering Shiraz University ShirazIranSchool of Mechanical Engineering Shiraz University ShirazIranSchool of Electrical and Computer Engineering Shiraz University ShirazIranSchool of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSwedenMunich School of Engineering Technical University of Munich MunichGermanyAbstract The growing use of wireless technologies in power systems has raised concerns about cybersecurity, particularly regarding GPS spoofing attacks (GSAs). These attacks manipulate GPS data, leading to modifications in the phase angle of phasor measurement units (PMUs). In this paper, a Deep‐learning GPS‐Spoofing Counteraction (DLGSC) algorithm is proposed, utilizing PMU data for GSA detection and PMU data correction. The algorithm incorporates a recurrent neural network (RNN) and a set of long short‐term memory (LSTM) units separately, for signal correction after attack detection. Unlike existing methods that struggle with simultaneous attacks or they are static methods, DLGSC tackles these challenges by leveraging deep learning techniques. By selecting appropriate features for GSA detection, DLGSC achieves accurate results. The algorithm is evaluated on standard IEEE 14‐bus and IEEE 39‐bus power systems, and its performance is compared to statistical, dynamic, and Deep Learning (DL) methods in the literature. Additionally, an experimental setup is designed to validate the algorithm in a laboratory environment. Results demonstrate the easy‐implementable DLGSC algorithm's satisfactory real‐time performance in various scenarios, such as load variations and noise, achieving over 98% accuracy. Notably, DLGSC is cable of detecting multiple GSAs on different PMUs.https://doi.org/10.1049/gtd2.12929cyber securitydeep learning structureGPS spoofinglong short‐term memory (LSTM) |
| spellingShingle | Mohammad Sabouri Sara Siamak Maryam Dehghani Mohsen Mohammadi Mohammad Hasan Asemani Mohammad Reza Hesamzadeh Vedran Peric Increasing the resiliency of power systems in presence of GPS spoofing attacks: A data‐driven deep‐learning algorithm IET Generation, Transmission & Distribution cyber security deep learning structure GPS spoofing long short‐term memory (LSTM) |
| title | Increasing the resiliency of power systems in presence of GPS spoofing attacks: A data‐driven deep‐learning algorithm |
| title_full | Increasing the resiliency of power systems in presence of GPS spoofing attacks: A data‐driven deep‐learning algorithm |
| title_fullStr | Increasing the resiliency of power systems in presence of GPS spoofing attacks: A data‐driven deep‐learning algorithm |
| title_full_unstemmed | Increasing the resiliency of power systems in presence of GPS spoofing attacks: A data‐driven deep‐learning algorithm |
| title_short | Increasing the resiliency of power systems in presence of GPS spoofing attacks: A data‐driven deep‐learning algorithm |
| title_sort | increasing the resiliency of power systems in presence of gps spoofing attacks a data driven deep learning algorithm |
| topic | cyber security deep learning structure GPS spoofing long short‐term memory (LSTM) |
| url | https://doi.org/10.1049/gtd2.12929 |
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