TY - GEN
T1 - Improving the Security of Cloud-Based Intelligent Transportation Systems
AU - Lidkea, Viktor
AU - Muresan, Radu
AU - Al-Dweik, Arafat
AU - Zhou, Shu
N1 - Funding Information:
This research is supported by the Ministry of Transportation Ontario (MTO) Highway Infrastructure Innovation Funding Program (HIIFP) grant No. 051938. The authors of this paper thank the MTO for their support.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Cloud computing technology is integral part to the advancement of intelligent transportation systems. Cloud computing, however, introduces new layers of security risks. In this paper, we propose an efficient system for improving the security of a cloud-based intelligent transportation system built with enhanced road side units and a data collection and analysis server. A convolutional neural network is used to classify encrypted images obtained by roadside units based on the type of vehicle on the road in real-time. The proposed system never fully decrypts the collected images, thus protecting drivers' personal information, such as location, licence plate, and vehicle contents. Keeping drivers' personal information safe is paramount, as attacks can be made against vulnerable parties if the attacker knows their location. The results show improved computational performance in comparison with a fully decrypting system, specifically a 29% increase in computational speed, while keeping the data secure.
AB - Cloud computing technology is integral part to the advancement of intelligent transportation systems. Cloud computing, however, introduces new layers of security risks. In this paper, we propose an efficient system for improving the security of a cloud-based intelligent transportation system built with enhanced road side units and a data collection and analysis server. A convolutional neural network is used to classify encrypted images obtained by roadside units based on the type of vehicle on the road in real-time. The proposed system never fully decrypts the collected images, thus protecting drivers' personal information, such as location, licence plate, and vehicle contents. Keeping drivers' personal information safe is paramount, as attacks can be made against vulnerable parties if the attacker knows their location. The results show improved computational performance in comparison with a fully decrypting system, specifically a 29% increase in computational speed, while keeping the data secure.
KW - Convolutional Neural Network
KW - Encryption
KW - Intelligent Transporation System
KW - Machine Learning
KW - Security
UR - http://www.scopus.com/inward/record.url?scp=85074066985&partnerID=8YFLogxK
U2 - 10.1109/CCECE.2019.8861723
DO - 10.1109/CCECE.2019.8861723
M3 - Conference contribution
AN - SCOPUS:85074066985
T3 - 2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019
BT - 2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019
Y2 - 5 May 2019 through 8 May 2019
ER -
