Physical Layer Security of Autonomous Driving: Secure Vehicle-to-Vehicle Communication in A Security Cluster

Authors: Na-Young Ahn, Dong Hoon Lee

Ad Hoc and Sensor Wireless Networks, 45 (3-4), 293-336, 2019
Ad Hoc & Sensor Wireless Networks; accepted on Nov. 21, 2019

Results of the summarizing process for the arXiv paper: 1912.06527v1

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The paper titled "Physical Layer Security of Autonomous Driving: Secure Vehicle-to-Vehicle Communication in A Security Cluster" by Na-Young Ahn and Dong Hoon Lee proposes a secure communication system for autonomous vehicles called Vehicle-to-Vehicle (V2V) communications in a secure cluster. The authors define the security cluster as a group of vehicles that possess a certain level or more of secrecy capacity. Defining secrecy capacity is often challenging, but the authors propose to define vehicular secrecy capacity based solely on Signal-to-Noise Ratio (SNR) values. This approach proves to be practical and efficient in achieving physical layer security in V2V communications. The secrecy capacity can be influenced by various parameters such as antenna-related, path-related, and noise-related parameters. In addition to these conventional parameters, the authors also consider unique vehicle-related parameters like vehicle speed, safety distance, speed limit, and response time in connection with autonomous driving. By modeling highway and urban traffic situations, they establish the relationship between these vehicle-related secrecy parameters and secrecy capacity. The proposed vehicular secrecy parameters enable real-time control of the vehicle's secrecy capacity in V2V communications. This control allows for secure communication even against potential attackers like quantum computers. The research presented in this paper contributes to achieving economic, effective, and efficient physical layer security in autonomous driving systems. Overall, this paper provides valuable insights into enhancing the security of V2V communications within a secure cluster by considering both conventional and vehicle-specific parameters. The findings contribute to the development of secure autonomous driving systems that can withstand potential threats from advanced technologies like quantum computers.
AI-generated keywords: Autonomous Driving V2V Security Cluster SNR Vehicle-Related

AI-generated Key Points

  • Paper proposes a secure communication system for autonomous vehicles called Vehicle-to-Vehicle (V2V) communications in a secure cluster
  • Security cluster defined as a group of vehicles with a certain level or more of secrecy capacity
  • Secrecy capacity defined based on Signal-to-Noise Ratio (SNR) values
  • Various parameters can influence secrecy capacity, including antenna-related, path-related, and noise-related parameters
  • Authors also consider vehicle-specific parameters like vehicle speed, safety distance, speed limit, and response time
  • Relationship between vehicle-related secrecy parameters and secrecy capacity established through modeling highway and urban traffic situations
  • Proposed vehicular secrecy parameters enable real-time control of the vehicle's secrecy capacity in V2V communications
  • Control allows for secure communication against potential attackers like quantum computers
  • Research contributes to achieving economic, effective, and efficient physical layer security in autonomous driving systems
  • Findings enhance the security of V2V communications within a secure cluster by considering both conventional and vehicle-specific parameters
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Abstract: We suggest secure Vehicle-to-Vehicle communications in a secure cluster. Here, the security cluster refers to a group of vehicles having a certain level or more of secrecy capacity. Usually, there are many difficulties in defining secrecy capacity, but we define vehicular secrecy capacity for the vehicle defined only by SNR values. Defined vehicular secrecy capacity is practical and efficient in achieving physical layer security in V2V. Typically, secrecy capacity may be changed by antenna related parameters, path related parameters, and noise related parameters. In addition to these conventional parameters, we address unique vehicle-related parameters, such as vehicle speed, safety distance, speed limit, response time, etc. in connection with autonomous driving. We confirm the relationship between vehicle-related secrecy parameters and secrecy capacity through modeling in highway and urban traffic situations. These vehicular secrecy parameters enable real-time control of vehicle secrecy capacity of V2V communications. We can use vehicular secrecy capacity to achieve secure vehicle communications from attackers such as quantum computers. Our research enables economic, effective and efficient physical layer security in autonomous driving.

Submitted to arXiv on 13 Dec. 2019

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