Detecting Sybil Attacks using Proofs of Work and Location in VANETs

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. Proofs 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 valid 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 manager 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.

B-Ride: Ride Sharing With Privacy-Preservation, Trust and Fair Payment Atop Public Blockchain

“…In our scheme, driver upon arriving to pickup location, he/she should send pickup to the blockchain. For LBS, it could be the Roadside units in Vehicular Adhoc Networks (VANETs) [23], where each RSU can verify if the location provided by a driver falls in its coverage or not.…”

Privacy-Preserving Smart Parking System Using Blockchain and Private Information Retrieval

“…C [1] C [2] C [3] C [4] C [5] C [6] C [7] C [8] C [9] C [10] C [11] C [12] C [13] C [14] C [15] C [16] C [17] C [18]…”

“…In addition, 47,000 gallons of gasoline are consumed which produces 728 tons of carbon dioxide on average per year in Los Angeles area alone [2]. Consequently, technology should be used to help drivers find and book vacant parking slots to avoid traffic congestion [3], air pollution, and wasting drivers' times [4].…”

Expansion of Cyber Attack Data From Unbalanced Datasets Using Generative Techniques

“…Our goal is to create a balanced dataset so that we can investigate through network packet flow for better intrusion detection and analysis. [19]- [21] To tackle the imbalanced dataset problem, we propose a GAN methodology to generate necessary synthetic attack samples in order to balance the dataset. Figure 1 illustrates the solution model from the data preprocessing to providing balance to the dataset approaches.…”