A Lightweight Real-time Detection Algorithm for Drone WiFi Hijacking

Due to WiFi protocols prioritized usability over security and adopted weak encryption because of resource constraints，consumer drones are vulnerable to malicious hijacking attacks when communicating with ground stations via WiFi. In order to carry out drone WiFi hijacking attack experiments, the research team built the first real drone WiFi hijacking attack dataset to address the scarcity of real attack samples in this field, covering multiple types such as De-Authentication attacks. At the same time, considering the limited computing resources of drones and the high real-time requirements of communications, the team used self-built datasets and public datasets to conduct multi-dimensional comparative experiments on existing algorithms, selected the XGBoost model that takes into account both detection accuracy and lightness as the basic framework, designed a three-level feature screening mechanism of variance threshold filtering-high correlation elimination-Boruta feature selection , and introduced a weighted cross entropy loss function to optimize learning performance, and developed a lightweight drone WiFi hijacking real-time detection algorithm. The experimental results show that this method can effectively detect drone WiFi hijacking attack traffic, and its comprehensive performance is better than the existing algorithms compared with the original XGBoost method, the accuracy of the proposed method reaches 96.3 , and the inference time is shortened by half, which has both high accuracy and lightness.