@Article{cmc.2020.011264,
AUTHOR = {Fei Li, Jiayan Zhang, Edward Szczerbicki, Jiaqi Song, Ruxiang Li , Renhong Diao},
TITLE = {Deep Learning-Based Intrusion System for Vehicular Ad Hoc  Networks},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {653--681},
URL = {http://www.techscience.com/cmc/v65n1/39588},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.011264}
}