Efficient drone hijacking detection using two-step GA-XGBoost
Efficient drone hijacking detection using two-step GA-XGBoost
复制标题
使用两步 GA-XGBoost 进行高效无人机劫持检测
DOI：
10.1016/j.sysarc.2019.101694
复制
发表时间：
2020-02-01
影响因子：
4.5
通讯作者：
Yi, Wang
中科院分区：
文献类型：
--
作者：
Feng, Zhiwei;Guan, Nan;Yi, Wang
随着民用无人机的快速增长，其安全问题遇到了重大挑战。一架商用无人机可能会被全球定位系统(GPS)劫持--这是对恐怖袭击等非法活动的欺骗性攻击。理想情况下，通过比较GPS和惯性导航系统(INS)估计的位置可以检测到此类攻击，但由于INS随着时间的推移积累的误差，结果可能总是出错。因此，在本文中，我们提出了一种利用GPS和惯性测量单元(IMU)数据检测GPS欺骗攻击的两步GA-XGBoost方法。然而，直接在无人机上调整合适的XGBoost参数值以获得较高的预测结果会消耗大量资源，这将影响无人机的实时性能。该方法将训练阶段分为机外训练阶段和机载训练阶段。离机步首先利用飞行日志对模型进行训练，然后利用遗传算法自动调整训练参数值。一旦离岸模型经过训练，它就可以上传到无人机上。为了使该方法适用于具有不同类型传感器的无人机，提高预测结果的准确性，在机载步骤中，当无人机开始执行任务时，对模型进行进一步的训练。在星上训练结束后，该方法切换到预测模式。此外，我们的方法不需要任何额外的板载硬件。对一架真实的四旋翼无人机的实验也表明，在每个采样时刻，被劫持和未被劫持情况下的检测正确率分别为96.3%和100%。此外，我们的方法可以在攻击开始后的1个S内达到100%的检测正确率。
With the fast growth of civilian drones, their security problems meet significant challenges. A commercial drone may be hijacked by Global Positioning System (GPS)-spoofing attacks for illegal activities, such as terrorist attacks. Ideally, comparing positions respectively estimated by GPS and Inertial Navigation System (INS) can detect such attacks, while the results may always get fault because of the accumulated errors over time in INS. Therefore, in this paper, we propose a two-step GA-XGBoost method to detect GPS-spoofing attacks that just uses GPS and Inertial Measurement Unit (IMU) data. However, tunning the proper values of XGBoost parameters directly on the drone to achieve high prediction results consumes lots of resources which would influence the real-time performance of the drone. The proposed method separates the training phase into offboard step and onboard step. In offboard step, model is first trained by flight logs, and the training parameter values are automatically tuned by Genetic Algorithm (GA). Once the offboard model is trained, it could be uploaded to drones. To adapt our method to drones with different types of sensors and improve the correctness of prediction results, in onboard step, the model is further trained when a drone starts a mission. After onboard training finishes, the proposed method switches to the prediction mode. Besides, our method does not require any extra onboard hardware. The experiments with a real quadrotor drone also show the detection correctness is 96.3% and 100% in hijacked and non-hijacked cases at each sampling time respectively. Moreover, our method can achieve 100% detection correctness just within 1 s just after the attacks start.