Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs.
Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs.
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DOI：
10.3390/s22020662
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发表时间：
2022-01-15
期刊：
影响因子：
--
通讯作者：
Kaabouch N
中科院分区：
文献类型：
--
作者：
Talaei Khoei T;Ismail S;Kaabouch N
无人驾驶飞行器容易受到几种网络攻击，包括全球定位系统欺骗。已经提出了几种检测此类攻击的技术。然而，全球定位系统欺骗事件的反复和频繁表明，需要有效的安全解决方案来保护无人机。在本文中，我们提出了两种动态选择技术：度量优化动态选择器和加权度量优化动态选择器，它们确定了最有效的分类器来检测此类攻击。我们提出了一种一步集成特征选择方法来识别和丢弃数据集中相关的和不重要的特征。我们使用10个机器学习模型实现了所提出的技术，并从准确率、检测概率、虚警概率、误检概率和处理时间四个评价指标方面比较了它们的性能。该方法动态地选择检测结果最好的分类器来检测攻击。结果表明，该动态模型的准确率为99.6%，检测概率为98.9%，虚警概率为1.56%，误检概率为1.09%，处理时间为1.24S。
Unmanned aerial vehicles are prone to several cyber-attacks, including Global Positioning System spoofing. Several techniques have been proposed for detecting such attacks. However, the recurrence and frequent Global Positioning System spoofing incidents show a need for effective security solutions to protect unmanned aerial vehicles. In this paper, we propose two dynamic selection techniques, Metric Optimized Dynamic selector and Weighted Metric Optimized Dynamic selector, which identify the most effective classifier for the detection of such attacks. We develop a one-stage ensemble feature selection method to identify and discard the correlated and low importance features from the dataset. We implement the proposed techniques using ten machine-learning models and compare their performance in terms of four evaluation metrics: accuracy, probability of detection, probability of false alarm, probability of misdetection, and processing time. The proposed techniques dynamically choose the classifier with the best results for detecting attacks. The results indicate that the proposed dynamic techniques outperform the existing ensemble models with an accuracy of 99.6%, a probability of detection of 98.9%, a probability of false alarm of 1.56%, a probability of misdetection of 1.09%, and a processing time of 1.24 s.
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