Devices that ensure vehicle and driver safety or provide services to drivers generate a substantial amount of network traffic. The traffic is transmitted to the In-Vehicle Network (IVN) depending on the defined function. Consequently, to quickly process a lot of traffic transmitted to the IVN, an advanced network protocol such as Automotive Ethernet is necessary. However, owing to the connectivity reinforcement between devices inside a vehicle and external networks, attack vectors and vulnerabilities can be easily inherited from an established Ethernet to Automotive Ethernet. The present study proposes a method for detecting and identifying abnormalities in Automotive Ethernet based on wavelet transform and deep convolutional neural network. First, we define attack scenarios and extract normal and abnormal data corresponding to these scenarios. Second, we conduct several preprocesses, such as fixing the packet size and normalizing the network image data. Finally, we conduct extensive evaluations of the proposed method's performance, considering the size of network image data and multi-resolution levels. The results demonstrate that the proposed method can effectively detect an abnormality. Furthermore, the results suggest that the our method is more effective in terms of time-cost compared to default ResNet and EfficientNet methods.
|Number of pages
|IEEE Transactions on Information Forensics and Security
|Published - 2023
Bibliographical notePublisher Copyright:
© 2005-2012 IEEE.
- automotive ethernet
- data reduction
- deep learning
- in-vehicle network
- Intrusion detection system
- wavelet transform
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Computer Networks and Communications