Okokpujie, Kennedy O. and MUGHOLE, KALIMUMBALO DANIELLA and Badejo, J. A. and Adetiba, E. (2022) Congestion Intrusion Detection-Based Method for Controller Area Network Bus: A Case for KIA SOUL Vehicle. International information and Engineering Technology Association, 9 (5). pp. 1298-1304.
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Abstract
In the vehicle industry, connectivity and autonomy are becoming increasingly important features. One of the most used protocols for in-vehicle communication is the Controller Area Network (CAN) bus which manages the communication between networked components. However, the CAN bus, despite its critical importance, lacks sufficient security features to protect its network as well as the overall car system. Thus, vehicle network security is becoming increasingly crucial. Methods of intrusion detection help to improve the security of the in-vehicle network. This work aims to provide a model that enables effective detection of attacks such as fuzzy, DoS, and impersonation using the Deep Feedforward Neural Network (DeepFNN) model as well as the Long Short- Term Memory model. Moreover, the LSTM model presents the most satisfying outcome in terms of precision and recall metrics.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | attacks, Controller Area Network (CAN) bus,deep feedforward neural network, long short-term memory, intrusion detection, in-vehicle network |
| Subjects: | T Technology > T Technology (General) T Technology > TJ Mechanical engineering and machinery |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Engineering Sciences |
| Depositing User: | AKINWUMI |
| Date Deposited: | 17 Nov 2023 09:07 |
| Last Modified: | 17 Nov 2023 09:07 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/17610 |
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