A Deep Learning-Based FDI Attack Detection Scheme for Secure Data Consolidation in Cooperative UxV Operations Using Zero Touch Networking

Unmanned any vehicles (UxVs), including aerial (UAVs), ground (UGVs), surface (USVs), and underwater (UUVs) vehicles, are transforming smart ecosystems by enhancing infrastructure, industry, and urban environments. However, the diverse technologies involved in UxVs make standardization and efficient management in ultra-dense networks challenging. Zero Touch Network (ZTN) technology leverages artificial intelligence and software-defined networks to automate network management, addressing these complexities but also introducing vulnerabilities to cyber threats such as False Data Injection (FDI) attacks. This paper introduces a secure data consolidation scheme where UxV operation data is validated with a deep learning model before being integrated into the ZTN for seamless management. A simplified transformer-based FDI attack detection model is proposed, enhanced through data augmentation and information gain-based feature selection. An experimental environment is established to demonstrate the feasibility of the proposed scheme, and it is shown that the proposed scheme outperforms existing methods.