University of Oulu

A. Ferdowsi, S. Ali, W. Saad and N. B. Mandayam, "Cyber-Physical Security and Safety of Autonomous Connected Vehicles: Optimal Control Meets Multi-Armed Bandit Learning," in IEEE Transactions on Communications, vol. 67, no. 10, pp. 7228-7244, Oct. 2019. doi: 10.1109/TCOMM.2019.2927570

Cyber-physical security and safety of autonomous connected vehicles : optimal control meets multi-armed bandit learning

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Author: Ferdowsi, Aidin1; Ali, Samad2; Saad, Walid1;
Organizations: 1Wireless@VT, Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA
2Centre for Wireless Communications (CWC), University of Oulu, Finland
3WINLAB, Dept. of ECE, Rutgers University, New Brunswick, NJ, USA
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 3.3 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2019-12-09


Autonomous connected vehicles (ACVs) rely on intra-vehicle sensors such as camera and radar as well as inter-vehicle communication to operate effectively which exposes them to cyber and physical attacks in which an adversary can manipulate sensor readings and physically control the ACVs. In this paper, a comprehensive control and learning framework is proposed to thwart cyber and physical attacks on ACV networks. First, an optimal safe controller for ACVs is derived to maximize the street traffic flow while minimizing the risk of accidents by optimizing the ACV speed and inter-ACV spacing. It is proven that the proposed controller is robust to physical attacks which aim at making ACV systems unstable. Next, two data injection attack (DIA) detection approaches are proposed to address cyber attacks on sensors and their physical impact on the ACV system. The proposed approaches rely on leveraging the stochastic behavior of the sensor readings and on the use of a multi-armed bandit (MAB) algorithm. It is shown that, collectively, the proposed DIA detection approaches minimize the vulnerability of ACV sensors against cyber attacks while maximizing the ACV system’s physical robustness. Simulation results show that the proposed optimal safe controller outperforms the current state of the art controllers by maximizing the robustness of ACVs to physical attacks. The results also show that the proposed DIA detection approaches, compared to Kalman filtering, can improve the security of ACV sensors against cyber attacks and ultimately improve the physical robustness of an ACV system.

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Series: IEEE transactions on communications
ISSN: 0090-6778
ISSN-E: 1558-0857
ISSN-L: 0090-6778
Volume: 67
Issue: 10
Pages: 7228 - 7244
DOI: 10.1109/TCOMM.2019.2927570
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This research was supported by the U.S. National Science Foundation under Grants OAC-1541105 and CNS-1446621.
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