Distributed State Estimation with Privacy Protection and Security Analysis under Joint Attacks

The cybersecurity issues in unmanned vessel networks have become increasingly prominent in recent years. To ensure the safe operation and data reliability of unmanned vessels, this paper focuses on investigating the distributed and hierarchical elastic state estimation, and security analysis of unmanned surface vessels (USVs) under joint attacks encompassing Denial of Service (DoS) together with False Data Injection (FDI) attacks. First and foremost, a privacy protection model based on random encryption technology is intended to analyze the effects of assaults on estimation error and system performance in different scenarios under joint FDI and DoS attacks, as well as FDI dual-channel attacks. Secondly, to resist FDI attacks, data communication is encrypted throughout the entire process with privacy protection. To defend against DoS attacks, an encrypted compensation mechanism is developed. The security of the system based on privacy protection is proven to be detectable against FDI attacks. The network environment's openness often leads to systems encountering joint attacks when transmitting data. These attacks compromise the authenticity of the physical system. Consequently, a distributed and hierarchical elastic state system is constructed to resist joint attacks, which is used for ascertaining the actual system state. Finally, the proposed techniques' effectiveness is shown through simulation and comparative experiments on the velocities and horizontal position states on an unmanned surface vehicle.