1. A deep learning-based intrusion detection system for in-vehicle networks.
- Author
-
Alqahtani, Hamed and Kumar, Gulshan
- Subjects
- *
DEEP learning , *INTRUSION detection systems (Computer security) , *CONVOLUTIONAL neural networks - 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]
- Published
- 2022
- Full Text
- View/download PDF