Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks
The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly incre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/14/4736 |
| _version_ | 1797526044237889536 |
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| author | Sk. Tanzir Mehedi Adnan Anwar Ziaur Rahman Kawsar Ahmed |
| author_facet | Sk. Tanzir Mehedi Adnan Anwar Ziaur Rahman Kawsar Ahmed |
| author_sort | Sk. Tanzir Mehedi |
| collection | DOAJ |
| description | The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security. |
| first_indexed | 2024-03-10T09:24:33Z |
| format | Article |
| id | doaj.art-41ad2585c7bf450fb10a8bd5a89f01d7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:24:33Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-41ad2585c7bf450fb10a8bd5a89f01d72023-11-22T04:55:18ZengMDPI AGSensors1424-82202021-07-012114473610.3390/s21144736Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular NetworksSk. Tanzir Mehedi0Adnan Anwar1Ziaur Rahman2Kawsar Ahmed3Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail 1902, BangladeshCentre for Cyber Security Research and Innovation (CSRI), Deakin University, Geelong 3216, AustraliaDepartment of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail 1902, BangladeshDepartment of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail 1902, BangladeshThe Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.https://www.mdpi.com/1424-8220/21/14/4736electric vehiclesin-vehicle networkcontroller area networkcybersecurityintrusion detectiondeep learning |
| spellingShingle | Sk. Tanzir Mehedi Adnan Anwar Ziaur Rahman Kawsar Ahmed Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks Sensors electric vehicles in-vehicle network controller area network cybersecurity intrusion detection deep learning |
| title | Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks |
| title_full | Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks |
| title_fullStr | Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks |
| title_full_unstemmed | Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks |
| title_short | Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks |
| title_sort | deep transfer learning based intrusion detection system for electric vehicular networks |
| topic | electric vehicles in-vehicle network controller area network cybersecurity intrusion detection deep learning |
| url | https://www.mdpi.com/1424-8220/21/14/4736 |
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