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A deep learning-based intrusion detection system for in-vehicle networks.
- Source :
-
Computers & Electrical Engineering . Dec2022:Part B, Vol. 104, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- Modern vehicles are increasingly getting connected within the vehicles, with other systems, leading to more concerns about security. Controller area network (CAN) has become a de-facto standard for connecting internal vehicles' components. However, it lacks security features. Conventional security mechanisms fail to protect in-vehicle networks from attacks, requiring the development of an effective intrusion detection system (IDS). This work develops an IDS for in-vehicle networks called IDS-IVN based on a compact representation of location invariant and time-variant traffic features using deep learning. The IDS-IVN uses convolutional neural and long–short-term memory networks as encoder/decoder functions of autoencoder networks to extract features from raw data and classify them using latent space representation into intrusive and non-intrusive classes. A benchmark real-time ROAD dataset is used to demonstrate the IDS-IVN's performance compared to the existing methods. IDS-IVN reports 99% accuracy with a 0.32% low false-positive rate for detecting intrusions. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00457906
- Volume :
- 104
- Database :
- Academic Search Index
- Journal :
- Computers & Electrical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 160366802
- Full Text :
- https://doi.org/10.1016/j.compeleceng.2022.108447