COTS Drone Detection using Video Streaming Characteristics

Anas Alsoliman, Giulio Rigoni, M. Levorato, C. Pinotti, Nils Ole Tippenhauer, M. Conti
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引用次数: 7

Abstract

Cheap commercial off-the-shelf (COTS) drones have become widely available for consumers in recent years. Unfortunately, they also provide low-cost capabilities for attackers. Therefore, effective methods to detect the presence of non-cooperating rogue drones within a restricted area are highly required. Approaches based on detection of control traffic have been proposed but were not yet shown to work against other benign traffic, such as that generated by wireless security cameras. In this work, we propose a novel drone detection framework based on a Random Forest classification model. In essence, the framework leverages specific patterns in video traffic transmitted by drones. The patterns consist of repetitive synchronization packets (denoted as pivots) which we use as features in the proposed machine learning classifier. We show that our framework can achieve up to 99% detection accuracy over an encrypted WiFi channel using only 20 packets originated from the drone. Our system is able to identify drone transmissions even among very similar WiFi transmission (such as a security camera video stream) and in a noisy scenario with background traffic.
基于视频流特性的COTS无人机检测
近年来,廉价的商用现成(COTS)无人机已广泛提供给消费者。不幸的是,它们也为攻击者提供了低成本的能力。因此,迫切需要有效的方法来检测限制区域内不合作的流氓无人机的存在。已经提出了基于控制流量检测的方法,但尚未证明对其他良性流量有效,例如无线安全摄像机产生的流量。在这项工作中,我们提出了一种新的基于随机森林分类模型的无人机检测框架。从本质上讲,该框架利用了无人机传输视频流量的特定模式。模式由重复的同步包(表示为枢轴)组成,我们将其用作提议的机器学习分类器中的特征。我们表明,我们的框架可以在加密的WiFi通道上实现高达99%的检测精度,仅使用来自无人机的20个数据包。我们的系统能够识别无人机传输,即使是在非常相似的WiFi传输(如安全摄像头视频流)和嘈杂的背景交通场景中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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