Dynamic Quasi-Hyperbolic Momentum Iterative Attack With Small Perturbation for 4D-Flight Trajectory Prediction

With the rapid growth of global air traffic, 4Dflight trajectory prediction (4D-FTP) using deep learning (DL) methods has become essential for applications, such as flight delay prediction, fuel consumption analysis, and traffic management. However, the adversarial attacks pose significant security threats to DL-based 4D-FTP systems reliant on automatic dependent surveillance-broadcast (ADS-B) sensors. Furthermore, existing vulnerabilities in time-series prediction (TSP) models for 4DFTP remain underexplored due to the lack of effective and stealthy attack methods. To address this gap, we propose the dynamic quasi-hyperbolic momentum (QHM) iterative attack with small perturbation (DQM-Attack). This method leverages QHM and dynamic step sizes to optimize gradient utilization in 4D-FTP models. In addition, attack stealth is enhanced through the manifold smooth module (MSM) and sparse smooth reinforcement learning (SS-RL). Experimental results demonstrate that DQM-Attack effectively disrupts predictions with minimal perturbations across four state-of-the-art TSP models. This study appears to be the first to address stealthy adversarial attacks on 4D-FTP systems, revealing a critical security vulnerability in air traffic management (ATM).