TY - JOUR
T1 - G-DriverAUT
T2 - A Growable Driver Authentication Scheme Based on Incremental Learning
AU - Xun, Yijie
AU - Guo, Wei
AU - Liu, Jiajia
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2023/5/1
Y1 - 2023/5/1
N2 - As an important part of intelligent and connected vehicles (ICVs) safety, driver authentication is a hot research direction in recent years. The existing driver authentication schemes in the industry are based on scret key, biometric characteristics or image characteristics, and these schemes always have efficiency and security problems in real-time driver authentication. In order to make up for these shortcomings, researchers propose driver authentication schemes based on driver behaviors and conduct in-depth explorations. However, the existing behavior-based schemes rely on a fixed number of drivers, when a new driver is added as an authorized user, existing schemes need to retrain the whole model to authenticate all drivers, cost many computing resources and can not meet the low latency requirements of ICVs. To solve the above deficiencies, we propose a growable driver authentication scheme based on incremental learning for the first time to improve authentication efficiency in dynamic scenarios where the number of drivers is constantly increasing. We extract driver behavior characteristic data from two real vehicles and design a growable model based on incremental learning for driver authentication. Besides these, we also use the support vector machine (SVM), convolutional neural network (CNN) and some other algorithms to make comparative experiments. The experimental results indicate that our scheme not only has high authentication accuracy, but also greatly reduces the time of model retraining.
AB - As an important part of intelligent and connected vehicles (ICVs) safety, driver authentication is a hot research direction in recent years. The existing driver authentication schemes in the industry are based on scret key, biometric characteristics or image characteristics, and these schemes always have efficiency and security problems in real-time driver authentication. In order to make up for these shortcomings, researchers propose driver authentication schemes based on driver behaviors and conduct in-depth explorations. However, the existing behavior-based schemes rely on a fixed number of drivers, when a new driver is added as an authorized user, existing schemes need to retrain the whole model to authenticate all drivers, cost many computing resources and can not meet the low latency requirements of ICVs. To solve the above deficiencies, we propose a growable driver authentication scheme based on incremental learning for the first time to improve authentication efficiency in dynamic scenarios where the number of drivers is constantly increasing. We extract driver behavior characteristic data from two real vehicles and design a growable model based on incremental learning for driver authentication. Besides these, we also use the support vector machine (SVM), convolutional neural network (CNN) and some other algorithms to make comparative experiments. The experimental results indicate that our scheme not only has high authentication accuracy, but also greatly reduces the time of model retraining.
KW - driver authentication
KW - incremental learning
KW - Intelligent and connected vehicles
UR - http://www.scopus.com/inward/record.url?scp=85147211712&partnerID=8YFLogxK
U2 - 10.1109/TVT.2022.3233739
DO - 10.1109/TVT.2022.3233739
M3 - 文章
AN - SCOPUS:85147211712
SN - 0018-9545
VL - 72
SP - 5759
EP - 5768
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 5
ER -