Malicious Vehicle Detection Scheme Based on Spatio-Temporal Features of Traffic Flow Under Cloud-Fog Computing-Based IoVs

Internet of vehicles (IoVs) is an important information exchange platform for intelligent transportation systems (ITSs) to provide traffic services. However, the appearance of malicious vehicles in IoVs can damage the security and stability of ITSs, which may provide false traffic data to cause serious traffic accidents. Also, many existing cryptography-based malicious vehicle detection scheme can only be used to resist some external attacks, while some internal malicious vehicles are easy to use their legal identities to provide false traffic data for other honest vehicles. In this paper, we propose a malicious vehicle detection scheme based on spatio-temporal features of traffic flow under cloud-fog computing-based IoVs. In our scheme, a traffic subarea division method based on spatial correlation degrees of road intersections is proposed to divide the urban road network into multiple traffic subareas. Based on the divided traffic subareas, an improved subarea-based graph attention model is proposed to extract the spatial features of traffic flow by the fog server. Then a gated recurrent unit method with attention mechanism is constructed to extract the temporal features of traffic flow by the cloud server, and a short-term traffic flow prediction model is built on the extracted spatio-temporal features of traffic flow. Further, a reputation calculation mechanism is established to score each vehicle by the fog server according to the verification of the traffic data uploaded by the vehicle and the traffic data predicted by our constructed prediction model, which is used to judge whether the vehicle is malicious according to its reputation score. Related experimental results show our scheme is effective and efficient to detect malicious vehicles under cloud-fog computing-based IoVs.