Open Access
ARTICLE
Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks
Fei Li1, *, Jiayan Zhang1, Edward Szczerbicki2, Jiaqi Song1, Ruxiang Li 1, Renhong Diao1
1 Chengdu University of Information Technology, Chengdu, 610225, China.
2 Gdansk University of Technology , Gdansk, 80-803, Poland.
* Corresponding Author: Fei Li. Email: .
Computers, Materials & Continua 2020, 65(1), 653-681. https://doi.org/10.32604/cmc.2020.011264
Received 29 April 2020; Accepted 22 May 2020; Issue published 23 July 2020
Abstract
The increasing use of the Internet with vehicles has made travel more
convenient. However, hackers can attack intelligent vehicles through various technical
loopholes, resulting in a range of security issues. Due to these security issues, the safety
protection technology of the in-vehicle system has become a focus of research. Using the
advanced autoencoder network and recurrent neural network in deep learning, we
investigated the intrusion detection system based on the in-vehicle system. We combined
two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the
detection of intrusive behavior. In order to verify the accuracy and efficiency of the
proposed model, it was evaluated using real vehicle data. The experimental results show
that the combination of the two technologies can effectively and accurately identify
abnormal boundary behavior. The parameters of the model are self-iteratively updated
using the time-based back propagation algorithm. We verified that the model proposed in
this study can reach a nearly 96% accurate detection rate.
Keywords
Cite This Article
F. Li, J. Zhang, E. Szczerbicki, J. Song, R. Li
et al., "Deep learning-based intrusion system for vehicular ad hoc networks,"
Computers, Materials & Continua, vol. 65, no.1, pp. 653–681, 2020.
Citations