A Sybil attack is caused by a malicious vehicle node stealing fake identities and continuously generating fake vehicles on the road to create the illusion of congestion, which endangers normal vehicles on the road. Because Sybil vehicle nodes have trajectories and motion states similar to those of normal vehicles, they are more difficult to detect in high-density traffic environments. The real-time authentication of vehicles is impossible in the existing traffic environment; thus, malicious vehicle nodes with a high degree of stealth can continuously attack and are difficult to stop. In this paper, we propose a Sybil attack detection method based on basic security message (BSM) packets, which exploits the characteristic that BSM packets have a unique sending source and uses the spatiotemporal relationship of dynamic vehicle location changes to detect and trace Sybil attacks. A weighted integration strategy is proposed for increasing the detection precision without machine-learning model prediction. Experimental results indicated that the proposed method can detect Sybil attacks in real time and is not affected by the attack density or traffic density. Moreover, it can detect Sybil nodes and trace malicious nodes simultaneously, with precisions of ¿98% and ¿94%, respectively, resolving the difficulties of existing detection schemes.