A Self-Learning Detection Method of Sybil Attack Based on LSTM for Electric Vehicles
Electric vehicles (EVs) are the development direction of new energy vehicles in the future. As an important part of the Internet of things (IOT) communication network, the charging pile is also facing severe challenges in information security. At present, most detection methods need a lot of prophet...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-03-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/6/1382 |
| _version_ | 1797999130911440896 |
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| author | Yi-Ying Zhang Jing Shang Xi Chen Kun Liang |
| author_facet | Yi-Ying Zhang Jing Shang Xi Chen Kun Liang |
| author_sort | Yi-Ying Zhang |
| collection | DOAJ |
| description | Electric vehicles (EVs) are the development direction of new energy vehicles in the future. As an important part of the Internet of things (IOT) communication network, the charging pile is also facing severe challenges in information security. At present, most detection methods need a lot of prophetic data and too much human intervention, so they cannot do anything about unknown attacks. In this paper, a self-learning-based attack detection method is proposed, which makes training and prediction a closed-loop system according to a large number of false information packets broadcast to the communication network. Using long short-term memory (LSTM) neural network training to obtain the characteristics of traffic data changes in the time dimension, the unknown malicious behavior characteristics are self-extracted and self-learning, improving the detection efficiency and quality. In this paper, we take the Sybil attack in the car network as an example. The simulation results show that the proposed method can detect the Sybil early attack quickly and accurately. |
| first_indexed | 2024-04-11T10:59:38Z |
| format | Article |
| id | doaj.art-7473d4c96804473dac7fe05e159c38fd |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T10:59:38Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-7473d4c96804473dac7fe05e159c38fd2022-12-22T04:28:40ZengMDPI AGEnergies1996-10732020-03-01136138210.3390/en13061382en13061382A Self-Learning Detection Method of Sybil Attack Based on LSTM for Electric VehiclesYi-Ying Zhang0Jing Shang1Xi Chen2Kun Liang3College of Artificial Intelligence, Tianjin University of Science & Technology, Tianjin 300457, ChinaCollege of Artificial Intelligence, Tianjin University of Science & Technology, Tianjin 300457, ChinaGEIRI North America; 250 W Tasman Dr., Ste 100, San Jose, CA 95134, USACollege of Artificial Intelligence, Tianjin University of Science & Technology, Tianjin 300457, ChinaElectric vehicles (EVs) are the development direction of new energy vehicles in the future. As an important part of the Internet of things (IOT) communication network, the charging pile is also facing severe challenges in information security. At present, most detection methods need a lot of prophetic data and too much human intervention, so they cannot do anything about unknown attacks. In this paper, a self-learning-based attack detection method is proposed, which makes training and prediction a closed-loop system according to a large number of false information packets broadcast to the communication network. Using long short-term memory (LSTM) neural network training to obtain the characteristics of traffic data changes in the time dimension, the unknown malicious behavior characteristics are self-extracted and self-learning, improving the detection efficiency and quality. In this paper, we take the Sybil attack in the car network as an example. The simulation results show that the proposed method can detect the Sybil early attack quickly and accurately.https://www.mdpi.com/1996-1073/13/6/1382evsybil attackintrusion detectionself-learning |
| spellingShingle | Yi-Ying Zhang Jing Shang Xi Chen Kun Liang A Self-Learning Detection Method of Sybil Attack Based on LSTM for Electric Vehicles Energies ev sybil attack intrusion detection self-learning |
| title | A Self-Learning Detection Method of Sybil Attack Based on LSTM for Electric Vehicles |
| title_full | A Self-Learning Detection Method of Sybil Attack Based on LSTM for Electric Vehicles |
| title_fullStr | A Self-Learning Detection Method of Sybil Attack Based on LSTM for Electric Vehicles |
| title_full_unstemmed | A Self-Learning Detection Method of Sybil Attack Based on LSTM for Electric Vehicles |
| title_short | A Self-Learning Detection Method of Sybil Attack Based on LSTM for Electric Vehicles |
| title_sort | self learning detection method of sybil attack based on lstm for electric vehicles |
| topic | ev sybil attack intrusion detection self-learning |
| url | https://www.mdpi.com/1996-1073/13/6/1382 |
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