In vehicular named data network (VNDN), interest flooding attack (IFA) occupies or even exhausts network resources by sending a large number of malicious interest packets, which results in the failure to meet the requests of legitimate users and seriously endangers the operation safety of Internet of Vehicles (IoV). To solve the problems, this study proposes an IFA detection method based on traffic monitoring. Firstly, a distributed network traffic monitoring layer based on RSU is constructed, where each RSU monitors the network traffic within its communication range, and the communication interconnection between RSUs forms the RSU network traffic monitoring layer. Secondly, a fixed time window is set, and the network traffic in each window is analyzed from three dimensions, i.e., information entropy, network self-similarity, and singularity. Additionally, a new field is added to the interest packet, and thus information entropy can be used to reflect the distribution of interest packet sources. Finally, the above three indicators are comprehensively employed to judge the existence of attack. The simulation results indicate that the proposed method effectively improves the accuracy of IFA detection and reduces the misjudgment rate.