| Title | False Data Injection Attack Detection in a Platoon of CACC in RSU |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Gao, Kai, Cheng, Xiangyu, Huang, Hao, Li, Xunhao, Yuan, Tingyu, Du, Ronghua |
| Conference Name | 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) |
| Keywords | Collaboration, data privacy, Data processing, false data injection attack, false trust, Intrusion detection, Monte Carlo methods, On Board Unit, Platoon, policy-based governance, pubcrawl, resilience, Resiliency, Roads, Scalability, Sequential importance sampling, threshold, Time factors |
| Abstract | Intelligent connected vehicle platoon technology can reduce traffic congestion and vehicle fuel. However, attacks on the data transmitted by the platoon are one of the primary challenges encountered by the platoon during its travels. The false data injection (FDI) attack can lead to road congestion and even vehicle collisions, which can impact the platoon. However, the complexity of the cellular - vehicle to everything (C-V2X) environment, the single source of the message and the poor data processing capability of the on board unit (OBU) make the traditional detection methods' success rate and response time poor. This study proposes a platoon state information fusion method using the communication characteristics of the platoon in C-V2X and proposes a novel platoon intrusion detection model based on this fusion method combined with sequential importance sampling (SIS). The SIS is a measured strategy of Monte Carlo integration sampling. Specifically, the method takes the status information of the platoon members as the predicted value input. It uses the leader vehicle status information as the posterior probability of the observed value to the current moment of the platoon members. The posterior probabilities of the platoon members and the weights of the platoon members at the last moment are used as input to update the weights of the platoon members at the current moment and obtain the desired platoon status information at the present moment. Moreover, it compares the status information of the platoon members with the desired status information to detect attacks on the platoon. Finally, the effectiveness of the method is demonstrated by simulation. |
| DOI | 10.1109/TrustCom56396.2022.00186 |
| Citation Key | gao_false_2022 |
False Data Injection Attack Detection in a Platoon of CACC in RSU