Detecting Sybil Attacks in Vehicular Fog Networks Using RSSI and Blockchain

Benadla, Sarra; Boudia, Omar Rafik Merad; Senouci, Sidi‐Mohammed; Lehsaini, Mohamed

doi:10.1109/tnsm.2022.3216073

Cited by 7 publications

(1 citation statement)

References 41 publications

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“…Yet, multiple vehicles may have the same RSSI value only if these vehicles belong to the same vehicle, which cannot defend against collusion in Sybil attacks. In addition, since power affects RSSI, this scheme can only detect malicious vehicles without power control but cannot detect malicious vehicles with transmission power control [40]. Zhang [41] proposed a detection method based on Basic Safety Message (BSM) packets in 2023, which mainly detects the detected vehicle by receiving the receiver (Recv) of the BSM message broadcast by the detected vehicle.…”

Section: Indirect Identity Detection Mechanismsmentioning

confidence: 99%

Sybil Attacks Detection and Traceability Mechanism Based on Beacon Packets in Connected Automobile Vehicles

Zhu,

Zeng,

Weng

et al. 2024

Sensors

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Connected Automobile Vehicles (CAVs) enable cooperative driving and traffic management by sharing traffic information between them and other vehicles and infrastructures. However, malicious vehicles create Sybil vehicles by forging multiple identities and sharing false location information with CAVs, misleading their decisions and behaviors. The existing work on defending against Sybil attacks has almost exclusively focused on detecting Sybil vehicles, ignoring the traceability of malicious vehicles. As a result, they cannot fundamentally alleviate Sybil attacks. In this work, we focus on tracking the attack source of malicious vehicles by using a novel detection mechanism that relies on vehicle broadcast beacon packets. Firstly, the roadside units (RSUs) randomly instruct vehicles to perform customized key broadcasting and listening within communication range. This allows the vehicle to prove its physical presence by broadcasting. Then, RSU analyzes the beacon packets listened to by the vehicle and constructs a neighbor graph between the vehicles based on the customized particular fields in the beacon packets. Finally, the vehicle’s credibility is determined by calculating the edge success probability of vehicles in the neighbor graph, ultimately achieving the detection of Sybil vehicles and tracing malicious vehicles. The experimental results demonstrate that our scheme achieves the real-time detection and tracking of Sybil vehicles, with precision and recall rates of 98.53% and 95.93%, respectively, solving the challenge of existing detection schemes failing to combat Sybil attacks from the root.

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Section: Indirect Identity Detection Mechanismsmentioning

confidence: 99%