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Detecting Sybil Attacks in VANET: Exploring Feature Diversity and Deep Learning Algorithms with Insights into Sybil Node Associations

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Abstract

Vehicular ad hoc networks (VANET) facilitate vehicle to everything (V2X) communication between vehicles and road side units (RSU) to exchange safety and alert messages required for the successful implementation of VANET applications. However, VANET is exposed to Sybil attacks, where malicious vehicles generate multiple false identities to seize control of network resources or create non-existent traffic situations. While several Sybil attack detection mechanisms based on deterministic and learning methods exist, their performance is restricted to particular scenarios. This work proposes a deep learning-based Sybil Attack Detection mechanism. It identifies the Sybil nodes by detecting the associativity between senders at a time instant using common vehicle characteristics, including Euclidean distance, speed, flow, and computed similarity between senders by using the dynamic time warping (DTW) method. Among the five applied deep learning models, the proposed solution achieves improved detection performance through convolutional neural network (CNN) and a combination of CNN with long short-term memory (LSTM) models in varying network scenarios.

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Data Availability

The VeReMi Extension dataset analysed during the current study is available on GitHub at https://github.com/josephkamel/VeReMi-Dataset.

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Funding

No funding was received to assist with the preparation of this manuscript.

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Author notes

  1. Rukhsar Sultana, Meenakshi Tripathi, Manhar Singh Sachdev, and Sparsh Taneja have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, JLN Road, Jaipur, 302017, Rajasthan, India

    Rukhsar Sultana, Jyoti Grover & Meenakshi Tripathi

  2. Google Bangalore, Bengaluru, 560016, Karnataka, India

    Manhar Singh Sachdev

  3. MakeMyTrip India Private Limited, Bengaluru, 560095, Karnataka, India

    Sparsh Taneja

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  1. Rukhsar Sultana
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  2. Jyoti Grover
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  3. Meenakshi Tripathi
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  4. Manhar Singh Sachdev
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  5. Sparsh Taneja
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Contributions

R.S.: Conceptualization, Methodology, Implementation, Writing - Original Draft, Revision. J.G.: Supervision, Conceptualization, Editing and Review.  M.T.: Supervision, Review. M.S.S.: Implementation. S.T.: Implmentation.

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Correspondence to Jyoti Grover.

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Sultana, R., Grover, J., Tripathi, M. et al. Detecting Sybil Attacks in VANET: Exploring Feature Diversity and Deep Learning Algorithms with Insights into Sybil Node Associations. J Netw Syst Manage 32, 51 (2024). https://doi.org/10.1007/s10922-024-09827-7

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