Intrusion Detection for Unmanned Aerial Vehicles Security: A Tiny Machine Learning Model

Unmanned aerial vehicles (UAVs) are vulnerable to network attacks. Designing an effective intrusion detection system (IDS) for UAVs is crucial. However, UAVs have limited computing resources and need to deal with massive amounts of network data, which further increases the difficulty of detection. Moreover, most existing IDSs have large parameters. In this study, we develop a tiny machine learning-based IDS to solve the above issue. We first establish an improved fuzzy rough set (FRS) model based on adaptive neighborhoods. Then, using the proposed FRS model, we employ a feature selection (FS) method to select optimal features and reduce the overall computational cost of the IDS. Furthermore, we proposed a tiny intrusion detection model that attains high-precision detection via shallow deep learning. Additionally, the proposed method can address intrusion detection problems in scenarios with partial data missing. According to the evaluations, the proposed method can effectively address intrusion detection in UAVs.