TY - GEN
T1 - Generative Adversarial Scheme Based GNSS Spoofing Detection for Digital Twin Vehicular Networks
AU - Liu, Hong
AU - Tu, Jun
AU - Liu, Jiawen
AU - Zhao, Zhenxue
AU - Zhou, Ruikang
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Digital twin vehicular network is an emerging architecture to realize vehicle communications. Anti-GNSS-spoofing becomes a challenging issue due to the growing automotive intelligence. However, the anti-spoofing methods are faced with several challenges: the additional cost of anti-spoofing devices, the limited computation resource within the vehicles, the lack of abnormal data, and model bias. To solve these problems, a generative adversarial scheme based anti-spoofing method is proposed for digital twin vehicular networks. The scheme consists of two deep-learning models of the generator and the detector, which generates pseudo normal data and detects spoofing. The LSTM model is introduced as the generator model, which fabricate the abnormal data with the GNSS/CAN/IMU data from Comma2k19. The DenseNet is introduced as the detector model, which make prediction on the basis of latitude, longitude, speed, steering angle and acceleration forward. The generative adversarial scheme is implemented for performance analysis, which indicates that the proposed scheme is suitable for digital twin vehicular applications.
AB - Digital twin vehicular network is an emerging architecture to realize vehicle communications. Anti-GNSS-spoofing becomes a challenging issue due to the growing automotive intelligence. However, the anti-spoofing methods are faced with several challenges: the additional cost of anti-spoofing devices, the limited computation resource within the vehicles, the lack of abnormal data, and model bias. To solve these problems, a generative adversarial scheme based anti-spoofing method is proposed for digital twin vehicular networks. The scheme consists of two deep-learning models of the generator and the detector, which generates pseudo normal data and detects spoofing. The LSTM model is introduced as the generator model, which fabricate the abnormal data with the GNSS/CAN/IMU data from Comma2k19. The DenseNet is introduced as the detector model, which make prediction on the basis of latitude, longitude, speed, steering angle and acceleration forward. The generative adversarial scheme is implemented for performance analysis, which indicates that the proposed scheme is suitable for digital twin vehicular applications.
KW - Automotive security
KW - Digital twin
KW - GNSS spoofing
UR - https://www.scopus.com/pages/publications/85115685190
U2 - 10.1007/978-3-030-86137-7_40
DO - 10.1007/978-3-030-86137-7_40
M3 - 会议稿件
AN - SCOPUS:85115685190
SN - 9783030861360
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 367
EP - 374
BT - Wireless Algorithms, Systems, and Applications - 16th International Conference, WASA 2021, Proceedings
A2 - Liu, Zhe
A2 - Wu, Fan
A2 - Das, Sajal K.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2021
Y2 - 25 June 2021 through 27 June 2021
ER -