Adversarial Attack Detection in Smart Grids Using Deep Learning Architectures
Smart grids themselves have emerged as vital structures of the up-to-date practical power systems or electricity networks that incorporate high technologies and information handling. Yet, they are more susceptible to an adversarial attack that can interfere with the critical functions like energy di...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10816619/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832583217408901120 |
|---|---|
| author | Stephanie Ness |
| author_facet | Stephanie Ness |
| author_sort | Stephanie Ness |
| collection | DOAJ |
| description | Smart grids themselves have emerged as vital structures of the up-to-date practical power systems or electricity networks that incorporate high technologies and information handling. Yet, they are more susceptible to an adversarial attack that can interfere with the critical functions like energy distribution and faults detection. This paper therefore proposes a new alternative to developing a DL and ML framework for identifying adversarial attacks on smart grids. After analyses of the performances of Logistic Regression, Perceptron, Gaussian Naive Bayes and Multi-Layer Perceptron, LSTM network has better results with an accuracy of 99.81%. The suggested framework strengthens smart grid immunity to cyber threats such as DoS attacks, back door injections, and adversarial perturbations while increasing energy distribution stability and security. For enhancing smart grid security, our results emphasize the importance of integration of ML and DL techniques and provide such an understanding of threat environment for future research and development on threat identification. |
| format | Article |
| id | doaj-art-36c93adc6d04483db47eebaef9a6a787 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-36c93adc6d04483db47eebaef9a6a7872025-01-29T00:01:02ZengIEEEIEEE Access2169-35362025-01-0113163141632310.1109/ACCESS.2024.352340910816619Adversarial Attack Detection in Smart Grids Using Deep Learning ArchitecturesStephanie Ness0https://orcid.org/0009-0004-9654-5722University of Vienna, Diplomatic Academy of Vienna, Vienna, AustriaSmart grids themselves have emerged as vital structures of the up-to-date practical power systems or electricity networks that incorporate high technologies and information handling. Yet, they are more susceptible to an adversarial attack that can interfere with the critical functions like energy distribution and faults detection. This paper therefore proposes a new alternative to developing a DL and ML framework for identifying adversarial attacks on smart grids. After analyses of the performances of Logistic Regression, Perceptron, Gaussian Naive Bayes and Multi-Layer Perceptron, LSTM network has better results with an accuracy of 99.81%. The suggested framework strengthens smart grid immunity to cyber threats such as DoS attacks, back door injections, and adversarial perturbations while increasing energy distribution stability and security. For enhancing smart grid security, our results emphasize the importance of integration of ML and DL techniques and provide such an understanding of threat environment for future research and development on threat identification.https://ieeexplore.ieee.org/document/10816619/Adversarial attackssmart gridslong short-term memory modelsperceptron |
| spellingShingle | Stephanie Ness Adversarial Attack Detection in Smart Grids Using Deep Learning Architectures IEEE Access Adversarial attacks smart grids long short-term memory models perceptron |
| title | Adversarial Attack Detection in Smart Grids Using Deep Learning Architectures |
| title_full | Adversarial Attack Detection in Smart Grids Using Deep Learning Architectures |
| title_fullStr | Adversarial Attack Detection in Smart Grids Using Deep Learning Architectures |
| title_full_unstemmed | Adversarial Attack Detection in Smart Grids Using Deep Learning Architectures |
| title_short | Adversarial Attack Detection in Smart Grids Using Deep Learning Architectures |
| title_sort | adversarial attack detection in smart grids using deep learning architectures |
| topic | Adversarial attacks smart grids long short-term memory models perceptron |
| url | https://ieeexplore.ieee.org/document/10816619/ |
| work_keys_str_mv | AT stephanieness adversarialattackdetectioninsmartgridsusingdeeplearningarchitectures |