Chapter
Jul 2, 2019
Analysis of Malicious Information Propagation on a Platoon of Connected Vehicles
Authors: Honggang Li, Yunshui Zhou, Guizhen Yu, Pengcheng Wang [email protected], and Sifen WangAuthor Affiliations
Publication: CICTP 2019
Abstract
Despite the highly promising future of connected vehicle technology, its communication through the wireless network in an open-access environment is vulnerable. Many previous studies focus on clarifying attack types and security frameworks but very few on the spreading of malicious information generated by cyberattacks. Therefore, it is necessary to understand the propagation pattern of malicious information. To this end, this paper introduces an analytical model, named vehicular malicious information propagation (VMIP), which integrates a classical epidemic model with a signal fading model using velocity and position information of platooned vehicles. The analytical and numerical results show that the proposed model can express the interactions between traffic dynamics and malicious information spreading. This paper is expected to contribute to a better understanding of the malicious information propagation in connected vehicles and lays a foundation for the future development of control strategies on mitigating the disastrous effects of cyberattacks.
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© 2019 American Society of Civil Engineers.
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Published online: Jul 2, 2019
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Honggang Li
Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang Univ., Beijing 100191, China; School of Transportation Science and Engineering, Beihang Univ., Xueyuan Rd. No. 37, Haidian District, Beijing 100191, China.
Yunshui Zhou
Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang Univ., Beijing 100191, China; School of Transportation Science and Engineering, Beihang Univ., Xueyuan Rd. No. 37, Haidian District, Beijing 100191, China.
Guizhen Yu
Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang Univ., Beijing 100191, China; School of Transportation Science and Engineering, Beihang Univ., Xueyuan Rd. No. 37, Haidian District, Beijing 100191, China.
Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang Univ., Beijing 100191, China; School of Transportation Science and Engineering, Beihang Univ., Xueyuan Rd. No. 37, Haidian District, Beijing 100191, China. E-mail: [email protected]
Sifen Wang
Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang Univ., Beijing 100191, China; School of Transportation Science and Engineering, Beihang Univ., Xueyuan Rd. No. 37, Haidian District, Beijing 100191, China.
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