Detecting Sybil Attacks in Vehicular Fog Networks Using RSSI and Blockchain

【Author】 Benadla, Sarra; Merad-Boudia, Omar Rafik; Senouci, Sidi Mohammed; Lehsaini, Mohamed

【Source】IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT

【影响因子】4.758

【Abstract】Vehicular Fog Computing (VFC) is a paradigm of vehicular networks that has a set of advantages such as agility, efficiency, and reduced latency. The VFC is vulnerable to a variety of attacks, and existing security measures in traditional networks are not necessarily applicable to VFC. Among these attacks, we can find the Sybil attack that allows a vehicle to create multiple identities to perform malicious operations. In this paper, we propose a blockchain-based mechanism to detect Sybil attacks in VFC networks. The detection process consists of two levels; the first one is targeted toward the verification of the vehicle's position by the FN using the Received Signal Strength Indicator (RSSI) technique. The FN delivers a position proof, if its position is valid, and stores it in the blockchain. At this point, the set of the obtained position proofs constitutes a trajectory. The second level is projected toward a comparison between the trajectories of the vehicles reporting an event. Two trajectories that pass through the same FNs at the same time, will be considered as Sybil trajectories. The objective of these two-level detections is to identify the Sybil attack in several attack scenarios performed by a powerful adversary. Our analysis shows that existing proposals cannot deal with such an adversary. Moreover, simulation results show the efficiency of our proposal in terms of communication, computation, and detection rate. Indeed, our system can reach a detection rate of 98% when the malicious vehicle generates several aliases simultaneously and sends position requests to the FN for each generated pseudonym.

【Keywords】Trajectory; Proposals; Cloud computing; Blockchains; Roads; Received signal strength indicator; Edge computing; Blockchain; RSSI; sybil attack; proof of location; trajectory; vehicular fog computing

【发表时间】2022 DEC

【收录时间】2023-05-14

【文献类型】实验仿真

【主题类别】

区块链应用-实体经济-交通领域