A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine
The smart grid is vulnerable to network attacks, thus requiring a high detection rate and fast detection speed for intrusion detection systems. With a fast training speed and a strong model generalization ability, the extreme learning machine (ELM) perfectly meets the needs of intrusion detection of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/18/4907 |
| _version_ | 1797553237575860224 |
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| author | Ke Zhang Zhi Hu Yufei Zhan Xiaofen Wang Keyi Guo |
| author_facet | Ke Zhang Zhi Hu Yufei Zhan Xiaofen Wang Keyi Guo |
| author_sort | Ke Zhang |
| collection | DOAJ |
| description | The smart grid is vulnerable to network attacks, thus requiring a high detection rate and fast detection speed for intrusion detection systems. With a fast training speed and a strong model generalization ability, the extreme learning machine (ELM) perfectly meets the needs of intrusion detection of the smart grid. In this paper, the ELM is applied to the field of smart grid intrusion detection. Aiming at the problem that the randomness of input weights and hidden layer bias in the ELM cannot guarantee the optimal performance of the ELM intrusion detection model, a genetic algorithm (GA)-ELM algorithm based on a genetic algorithm (GA) is proposed. GA is used to optimize the input weight and hidden layer bias of the ELM. Firstly, the input weight and hidden layer bias of the ELM are mapped to the chromosome vector of a GA, and the test error of the ELM model is set as the fitness function of the GA. Then, the parameters of the ELM intrusion detection model are optimized by genetic operation; the input weight and bias, corresponding to the minimum test error, are selected to improve the performance of the ELM model. Compared with the ELM and online sequential extreme learning machine (OS-ELM), the GA-ELM effectively improves the accuracy, detection rate and precision of intrusion detection and reduces the false positive rate and missing report rate. |
| first_indexed | 2024-03-10T16:12:32Z |
| format | Article |
| id | doaj.art-609624358eb248a0ae1e662c1334b0ef |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T16:12:32Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-609624358eb248a0ae1e662c1334b0ef2023-11-20T14:17:31ZengMDPI AGEnergies1996-10732020-09-011318490710.3390/en13184907A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning MachineKe Zhang0Zhi Hu1Yufei Zhan2Xiaofen Wang3Keyi Guo4School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaGlasgow College, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaCourant Institute of Mathematical Science, New York University, New York, NY 10003, USAThe smart grid is vulnerable to network attacks, thus requiring a high detection rate and fast detection speed for intrusion detection systems. With a fast training speed and a strong model generalization ability, the extreme learning machine (ELM) perfectly meets the needs of intrusion detection of the smart grid. In this paper, the ELM is applied to the field of smart grid intrusion detection. Aiming at the problem that the randomness of input weights and hidden layer bias in the ELM cannot guarantee the optimal performance of the ELM intrusion detection model, a genetic algorithm (GA)-ELM algorithm based on a genetic algorithm (GA) is proposed. GA is used to optimize the input weight and hidden layer bias of the ELM. Firstly, the input weight and hidden layer bias of the ELM are mapped to the chromosome vector of a GA, and the test error of the ELM model is set as the fitness function of the GA. Then, the parameters of the ELM intrusion detection model are optimized by genetic operation; the input weight and bias, corresponding to the minimum test error, are selected to improve the performance of the ELM model. Compared with the ELM and online sequential extreme learning machine (OS-ELM), the GA-ELM effectively improves the accuracy, detection rate and precision of intrusion detection and reduces the false positive rate and missing report rate.https://www.mdpi.com/1996-1073/13/18/4907smart gridadvanced metering infrastructure (AMI)extreme learning machine (ELM)intrusion detection system (IDS) |
| spellingShingle | Ke Zhang Zhi Hu Yufei Zhan Xiaofen Wang Keyi Guo A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine Energies smart grid advanced metering infrastructure (AMI) extreme learning machine (ELM) intrusion detection system (IDS) |
| title | A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine |
| title_full | A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine |
| title_fullStr | A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine |
| title_full_unstemmed | A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine |
| title_short | A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine |
| title_sort | smart grid ami intrusion detection strategy based on extreme learning machine |
| topic | smart grid advanced metering infrastructure (AMI) extreme learning machine (ELM) intrusion detection system (IDS) |
| url | https://www.mdpi.com/1996-1073/13/18/4907 |
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