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Learning-based VANET Communication and Security Techniques pp 13–47Cite as

Learning-Based Rogue Edge Detection in VANETs with Ambient Radio Signals

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Part of the book series: Wireless Networks ((WN))

Abstract

Rogue edge detection in VANETs is more challenging than the spoofing detection in indoor wireless networks due to the high mobility of onboard units and the large-scale network infrastructure with roadside units. In this chapter, we propose a physical-layer rogue edge detection scheme for VANETs according to the shared ambient radio signals observed during the same moving trace of the mobile device and the serving edge in the same vehicle. We also propose a privacy-preserving proximity-based security system for location-based services (LBS) in wireless networks, without requiring any pre-shared secret, trusted authority or public key infrastructure. In this scheme, the edge node under test has to send the physical properties of the ambient radio signals, including the received signal strength indicator (RSSI) of the ambient signals with the corresponding source media access control address during a given time slot. The mobile device can choose to compare the received ambient signal properties and its own record or apply the RSSI of the received signals to detect rogue edge attacks, and determines test threshold in the detection. Finally, we use a reinforcement learning technique to enable the mobile device to achieve the optimal detection policy in the dynamic VANET without being aware of the VANET model and the attack model.

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Notes

  1. 1.

    This system can be directly extended to the case with Alice connecting to multiple peer clients.

  2. 2.

    The duration is assumed to be short enough to avoid the reuse of SN for a given radio source.

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Authors and Affiliations

  1. Department of Communication Engineering, Xiamen University, Xiamen, Fujian, China

    Liang Xiao

  2. Department of Electrical & Computer Engineering, University of Waterloo, Waterloo, ON, Canada

    Weihua Zhuang

  3. Department of Electronic Engineering, Tsinghua University, Beijing, China

    Sheng Zhou

  4. Department of Automation, Shanghai Jiao Tong University, Shanghai, China

    Cailian Chen

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  1. Liang Xiao
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  2. Weihua Zhuang
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  3. Sheng Zhou
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  4. Cailian Chen
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Xiao, L., Zhuang, W., Zhou, S., Chen, C. (2019). Learning-Based Rogue Edge Detection in VANETs with Ambient Radio Signals. In: Learning-based VANET Communication and Security Techniques. Wireless Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-01731-6_2

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  • DOI: https://doi.org/10.1007/978-3-030-01731-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01730-9

  • Online ISBN: 978-3-030-01731-6

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