Improving the Security of Cloud-Based Intelligent Transportation Systems

Viktor Lidkea; Radu Muresan; Arafat Al-Dweik; Shu Zhou

doi:10.1109/CCECE.2019.8861723

Improving the Security of Cloud-Based Intelligent Transportation Systems

Viktor Lidkea
, Radu Muresan
, Arafat Al-Dweik
, Shu Zhou

Electrical Engineering

University of Guelph

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

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.

Original language	British English
Title of host publication	2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728103198
DOIs	https://doi.org/10.1109/CCECE.2019.8861723
State	Published - May 2019
Event	2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019 - Edmonton, Canada Duration: 5 May 2019 → 8 May 2019

Publication series

Name	2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019

Conference

Conference	2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019
Country/Territory	Canada
City	Edmonton
Period	5/05/19 → 8/05/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Convolutional Neural Network
Encryption
Intelligent Transporation System
Machine Learning
Security

Access to Document

10.1109/CCECE.2019.8861723

OpenUrl availability

Full text

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Lidkea, V., Muresan, R., Al-Dweik, A., & Zhou, S. (2019). Improving the Security of Cloud-Based Intelligent Transportation Systems. In 2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019 Article 8861723 (2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCECE.2019.8861723

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abstract = "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.",

keywords = "Convolutional Neural Network, Encryption, Intelligent Transporation System, Machine Learning, Security",

author = "Viktor Lidkea and Radu Muresan and Arafat Al-Dweik and Shu Zhou",

note = "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: {\textcopyright} 2019 IEEE.; 2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019 ; Conference date: 05-05-2019 Through 08-05-2019",

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Lidkea, V, Muresan, R, Al-Dweik, A & Zhou, S 2019, Improving the Security of Cloud-Based Intelligent Transportation Systems. in 2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019., 8861723, 2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019, Edmonton, Canada, 5/05/19. https://doi.org/10.1109/CCECE.2019.8861723

Improving the Security of Cloud-Based Intelligent Transportation Systems. / Lidkea, Viktor; Muresan, Radu; Al-Dweik, Arafat et al.
2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8861723 (2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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.

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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.

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KW - Convolutional Neural Network

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KW - Machine Learning

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Lidkea V, Muresan R, Al-Dweik A, Zhou S. Improving the Security of Cloud-Based Intelligent Transportation Systems. In 2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8861723. (2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019). doi: 10.1109/CCECE.2019.8861723

Improving the Security of Cloud-Based Intelligent Transportation Systems

Abstract

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UN SDGs

Keywords

Access to Document

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