A SURVEY OF SYBIL ATTACK DETECTION IN VANETS
M. Preetha, E. Pavithra, P. Sudharshna, S. Rakshini · International Journal of Engineering Applied Sciences and Technology · 2020
Vehicular unplanned Networks (VANETs) bring many benefits and conveniences to road safety and future transportation systems.Sybil attack is one among the foremost risky threats since it violates the elemental assumption of VANETsbased applications that each one received information are correct and trusted.Sybil attacker can generate multiple fake identities to false messages.In this paper, we proposed to completely unique Sybil attack detection method for supported by Received Signal Strength Indicator (RSSI), time series, Voiceprint, to conduct widely applicable, lightweight and full-distributed detection for VANETs.Voiceprint adopts RSSI statistic time series as vehicular speech and compares the similarity among all received series.Voiceprint doesn't believe any predefined radio propagation model, and conducts independent detection without support of centralized nodes.We improve Voiceprint for allowing to conduct detection vehicle on Service Channel (SCH) to observation time.And, we extend Voiceprint with change-points detection to identify those illegitimate nodes performing power control.Extensive simulations and real-world experiments demonstrate that Voiceprint is an efficient method considering the worth, complexity and performance.