Phantom Jam Sybil Attack Against Vehicular Networks

Abstract

Vehicular Ad Hoc Networks (VANETs) rely on Basic Safety Messages (BSMs) to support safety-critical applications such as collision avoidance and traffic awareness. However, BSMs can be exploited in Sybil attacks, where adversaries generate multiple ghost vehicles to manipulate traffic conditions. In this work, we introduce Phantom Jam, a motion-consistent Sybil attack designed to induce large-scale traffic disruptions while maintaining temporally consistent vehicle behavior. Unlike traditional Sybil attacks that rely on deterministic motion patterns, Phantom Jam combines map-aware trajectory replay with generative temporal modeling. Specifically, TimeGAN is used to synthesize plausible braking and acceleration dynamics, enabling ghost vehicles to emulate natural driving behavior during slowdown and recovery phases. We evaluate Phantom Jam using the F2MD simulation framework and the LuST Nano traffic scenario, with a recent deep-learning-based misbehavior detection system as the benchmark. Our experimental results show that Phantom Jam can reduce recall to as low as 0.66, indicating that a substantial portion of malicious vehicles remain undetected. Our work demonstrates that plausible temporal dynamics in Sybil attacks can pose significant challenges for modern VANET misbehavior detection systems.