Intrusion Detection Framework for Invasive FPV Drones Using Video Streaming Characteristics
Intrusion Detection Framework for Invasive FPV Drones Using Video Streaming Characteristics
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DOI：
10.1145/3579999
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发表时间：
2023-01
影响因子：
2.3
通讯作者：
Anas Alsoliman;Giulio Rigoni;Davide Callegaro;M. Levorato;C. Pinotti;M. Conti
中科院分区：
文献类型：
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作者：
Anas Alsoliman;Giulio Rigoni;Davide Callegaro;M. Levorato;C. Pinotti;M. Conti
近年来，廉价的商用现成（COTS）第一人称视角（FPV）无人机已广泛用于消费者。不幸的是，它们也为恶意用户提供了低成本的攻击机会。因此，非常需要有效的方法来检测受限区域内未知和不合作的无人机的存在。已经提出了基于发射的视频流检测无人机的方法，但尚未显示出对其他类似的良性流量（例如由无线安全摄像机生成的流量）起作用。最重要的是，这些方法没有在检测新的未定型无人机类型的背景下进行研究。在这项工作中，我们提出了一种新的无人机检测框架，它利用了无人机传输的视频流量中的特定模式。这些模式由重复的同步数据包（我们称之为pivots）组成，我们将其用作机器学习分类器的特征。我们表明，我们的框架可以实现高达99%的检测准确性在加密的WiFi信道使用只有170个数据包起源于无人机在820毫秒的时间段内。我们的框架能够识别无人机传输，即使在非常相似的WiFi传输（例如来自安全摄像头的视频流）以及具有背景流量的嘈杂场景中。此外，我们的枢轴功能的设计使分类器能够检测未分析的无人机，其中分类器从未训练过，并使用一种新的特征选择策略进行改进，该策略选择具有检测新的未分析无人机的区分能力的特征。
Cheap commercial off-the-shelf (COTS) First-Person View (FPV) drones have become widely available for consumers in recent years. Unfortunately, they also provide low-cost attack opportunities to malicious users. Thus, effective methods to detect the presence of unknown and non-cooperating drones within a restricted area are highly demanded. Approaches based on detection of drones based on emitted video stream have been proposed, but were not yet shown to work against other similar benign traffic, such as that generated by wireless security cameras. Most importantly, these approaches were not studied in the context of detecting new unprofiled drone types. In this work, we propose a novel drone detection framework, which leverages specific patterns in video traffic transmitted by drones. The patterns consist of repetitive synchronization packets (we call pivots), which we use as features for a machine learning classifier. We show that our framework can achieve up to 99% in detection accuracy over an encrypted WiFi channel using only 170 packets originated from the drone within 820ms time period. Our framework is able to identify drone transmissions even among very similar WiFi transmissions (such as video streams originated from security cameras) as well as in noisy scenarios with background traffic. Furthermore, the design of our pivot features enables the classifier to detect unprofiled drones in which the classifier has never trained on and is refined using a novel feature selection strategy that selects the features that have the discriminative power of detecting new unprofiled drones.