Due to the development of vehicle-to-vehicle (V2V) communication, safe driving support such as collision prevention and adaptive cruise control has been achieved. In addition, vehicle-to-infrastructure (V2I) communication as well as communication with a cloud server using mobile lines (vehicle-to-cloud communication) have been developed in recent years. These communications are altogether called vehicle-to-everything (V2X) communication. Through V2X communication, a vehicle's peripheral information can be shared with other vehicles on a cloud server. However, the problem of masquerade attacks on the cloud must be addressed. By faking vehicle information on a cloud server, an adversary may deliberately cause traffic congestion and/or accidents. In this research, we proposed a method that detects misbehavior (masquerade data) from ag-gregated data on a cloud server using V2X communication by utilizing the surrounding vehicle information. We also analyzed possible threats and requirements for data that are sent to cloud servers, and evaluate the proposed method's implementation. Using the proposed method, we detected 93% of the masquerade data, improved the detection rate by 100% by increasing the threshold value of the proposed method, and enhanced the effect of guaranteeing the data's reliability. Furthermore, we evaluated the false positives of the proposed method and its execution processing time, examining the method's feasibility.