Real-Time Intrusion Detection in Power Grids Using Deep Learning: Ensuring DPU Data Security
Deep learning technologies have revolutionized the management of energy, energy consumption, and data security within smart grids through non-intrusive load monitoring (NILM). This paper explores the use of deep learning for real-time intrusion detection in power grids with a primary focus on safegu...
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Ital Publication
2024-09-01
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| Series: | HighTech and Innovation Journal |
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| Online Access: | https://hightechjournal.org/index.php/HIJ/article/view/766 |
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| author | Maoran Xiao Qi Zhou Zhen Zhang Junjie Yin |
| author_facet | Maoran Xiao Qi Zhou Zhen Zhang Junjie Yin |
| author_sort | Maoran Xiao |
| collection | DOAJ |
| description | Deep learning technologies have revolutionized the management of energy, energy consumption, and data security within smart grids through non-intrusive load monitoring (NILM). This paper explores the use of deep learning for real-time intrusion detection in power grids with a primary focus on safeguarding the integrity and security of Data Processing Units (DPUs). An evaluation of various machine learning models, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Decision Trees, and Random Forests, is conducted to detect various types of intrusions, including Fault, Injection, Masquerade, Normal, and Replay. Random Forest produced AUC values of 1.00 for all classes and an overall F1-score of 0.99 for all classes. The Decision Tree model also shows robust performance for detecting Fault and Injection intrusions (AUC = 0.98), with an overall F1-score of 0.94. However, the LDA and SVM models do not perform well in detecting Injection intrusions with overall F1-scores of 0.83 and 0.86. Advances in machine learning can be used to improve smart grid security, reliability, and efficiency, according to this study. These findings highlight the potential of advanced machine learning techniques to enhance smart grid reliability and efficiency.
Doi: 10.28991/HIJ-2024-05-03-018
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| format | Article |
| id | doaj-art-1d4adca539c74fad95ddbf136f797208 |
| institution | OA Journals |
| issn | 2723-9535 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | HighTech and Innovation Journal |
| spelling | doaj-art-1d4adca539c74fad95ddbf136f7972082025-08-20T01:47:33ZengItal PublicationHighTech and Innovation Journal2723-95352024-09-015381482710.28991/HIJ-2024-05-03-018214Real-Time Intrusion Detection in Power Grids Using Deep Learning: Ensuring DPU Data SecurityMaoran Xiao0Qi Zhou1Zhen Zhang2Junjie Yin31) State Grid Jiangsu Electric Power Co., Ltd. Limited Information and Telecommunication Branch, Nanjing, Jiangsu, 210000, China. 2) State Grid Jiangsu Electric Power Co., Ltd. Wuxi Power Supply Branch, Wuxi, Jiangsu, 214000, China.State Grid Jiangsu Electric Power Co., Ltd. Wuxi Power Supply Branch, Wuxi, Jiangsu, 214000,State Grid Jiangsu Electric Power Co., Ltd. Limited Information and Telecommunication Branch, Nanjing, Jiangsu, 210000,State Grid Jiangsu Electric Power Co., Ltd. Limited Information and Telecommunication Branch, Nanjing, Jiangsu, 210000,Deep learning technologies have revolutionized the management of energy, energy consumption, and data security within smart grids through non-intrusive load monitoring (NILM). This paper explores the use of deep learning for real-time intrusion detection in power grids with a primary focus on safeguarding the integrity and security of Data Processing Units (DPUs). An evaluation of various machine learning models, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Decision Trees, and Random Forests, is conducted to detect various types of intrusions, including Fault, Injection, Masquerade, Normal, and Replay. Random Forest produced AUC values of 1.00 for all classes and an overall F1-score of 0.99 for all classes. The Decision Tree model also shows robust performance for detecting Fault and Injection intrusions (AUC = 0.98), with an overall F1-score of 0.94. However, the LDA and SVM models do not perform well in detecting Injection intrusions with overall F1-scores of 0.83 and 0.86. Advances in machine learning can be used to improve smart grid security, reliability, and efficiency, according to this study. These findings highlight the potential of advanced machine learning techniques to enhance smart grid reliability and efficiency. Doi: 10.28991/HIJ-2024-05-03-018 Full Text: PDFhttps://hightechjournal.org/index.php/HIJ/article/view/766machine learningintrusion detectionsmart gridsdata integritysecuritynilmreal-time detectionenergy management. |
| spellingShingle | Maoran Xiao Qi Zhou Zhen Zhang Junjie Yin Real-Time Intrusion Detection in Power Grids Using Deep Learning: Ensuring DPU Data Security HighTech and Innovation Journal machine learning intrusion detection smart grids data integrity security nilm real-time detection energy management. |
| title | Real-Time Intrusion Detection in Power Grids Using Deep Learning: Ensuring DPU Data Security |
| title_full | Real-Time Intrusion Detection in Power Grids Using Deep Learning: Ensuring DPU Data Security |
| title_fullStr | Real-Time Intrusion Detection in Power Grids Using Deep Learning: Ensuring DPU Data Security |
| title_full_unstemmed | Real-Time Intrusion Detection in Power Grids Using Deep Learning: Ensuring DPU Data Security |
| title_short | Real-Time Intrusion Detection in Power Grids Using Deep Learning: Ensuring DPU Data Security |
| title_sort | real time intrusion detection in power grids using deep learning ensuring dpu data security |
| topic | machine learning intrusion detection smart grids data integrity security nilm real-time detection energy management. |
| url | https://hightechjournal.org/index.php/HIJ/article/view/766 |
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