Turkish Journal of Computer and Mathematics Education
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Journal

Turkish Journal of Computer and Mathematics Education


Volume
& Issue

Volume 14, Issue 3


Published
on


Pages

857-868


DOI

Article

Detecting Sybil Attacks using Proofs of Work and Location in VANETs


Authors

Sanjeevini S.h.* Affiliation:
Assistant Professor, Department of CSE(CS), MallaReddy Engineering College for Women, Hyderabad, TS, India
, L. Savithri Affiliation:
Department of CSE-Cyber Security, Malla Reddy Engineering College for Women (A), Maisammaguda, Medchal, Telangana
, E. Maha Lakshmi Affiliation:
Department of CSE-Cyber Security, Malla Reddy Engineering College for Women (A), Maisammaguda, Medchal, Telangana
, M. Amulya Affiliation:
Department of CSE-Cyber Security, Malla Reddy Engineering College for Women (A), Maisammaguda, Medchal, Telangana
and P Sanjana Affiliation:
UG Students, Department of CSE(CS), MallaReddy Engineering College for Women, Hyderabad, TS, India


Abstract

Vehicular Ad Hoc Networks (VANETs) has the potential to enable the next -generation Intelligent Transportation Systems (ITS). In ITS, data contributed from vehicles can build a spatiotemporal view of traffic statistics, which can consequently improve road safety and reduce slow traffic and jams. To preserve vehicles’ privacy, vehicles should use multiple pseudonyms instead of only one identity. However, vehicles may exploit this abundance of pseudonyms and launch Sybil attacks by pretending to be multiple vehicles. Then, these Sybil (or fake) vehicles report false data, e.g., to create fake congestion or pollute traffic management data. In this paper, we propose a Sybil attack detection scheme using proofs of work and location. The idea is that each road side unit (RSU) issues a signed time-stamped tag as a proof for the vehicle’s anonymous location. Proof s sent from multiple consecutive RSUs is used to create vehicle trajectory which is used as vehicle anonymous identity. Also, one RSU is not able to issue trajectories for vehicles, rather the contributions of several RSUs are needed. By this way, attackers need to compromise an infeasible number of RSUs to create fake trajectories. Moreover, upon receiving the proof of location from an RSU, the vehicle should solve a computational puzzle by running proof of work (PoW) algorithm. So, it should provide a val id solution (proof of work) to the next RSU before it can obtain a proof of location. Using the PoW can prevent the vehicles from creating multiple trajectories in case of low -dense RSUs. Then, during any reported event, e.g., road congestion, the event ma nager uses a matching technique to identify the trajectories sent from Sybil vehicles. The scheme depends on the fact that the Sybil trajectories are bounded physically to one vehicle; therefore, their trajectories should overlap. Extensive experiments and simulations demonstrate that our scheme achieves high detection rate to Sybil attacks with low false negative and acceptable communication and computation overhead.


Keywords


Citation

Savithri, L., Lakshmi, E. M., Amulya, M., & Sanjana, P. (2023). Detecting sybil attacks using proofs of work and location in vanets. Turkish Journal of Computer and Mathematics Education, 14(3), 857–868.

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