论文标题
使用模糊测试的消费者无人机网络安全脆弱性评估
Consumer UAV Cybersecurity Vulnerability Assessment Using Fuzzing Tests
论文作者
论文摘要
无人驾驶汽车(UAV)是能够飞行的遥控车辆,并且在从军事行动到家庭享受的各种环境中。这些车辆是很棒的资产,但是就像他们的飞行员可以远程控制他们一样,网络攻击也可以以类似的方式执行。网络对无人机的攻击可以为物理和虚拟系统带来大量问题。这样的故障能够使攻击者能够窃取数据,无人机或劫持无人机的能力。为了减轻此类攻击,有必要识别和修补可能被恶意利用的漏洞。在本文中,使用与特定端口发送的大量数据流相关的相关无人机安全实践探索了一个新的无人机漏洞。更深入的模型涉及一系列数据字符串,涉及以模糊测试的形式发送到UAV的FTP端口的FTP特异性关键字,并在无人机上的其他端口也启动了数千个数据包。在这些测试中,对虚拟和物理系统进行了广泛的监视,以识别特定的模式和漏洞。该模型应用于鹦鹉bebop 2,该模型准确地描绘了一个无人机,其网络被攻击者妥协,并描绘了许多用于国内使用的低端无人机模型。在测试过程中,监测鹦鹉Bebop 2的GPS性能,视频速度,无人机对飞行员的反应性,运动功能以及无人机传感器数据的准确性的降解。所有这些监视点都可以全面了解无人机对每个单独测试的反应。在本文中,将讨论对抗此漏洞的对策,并可能会从模糊测试中分支出来。
Unmanned Aerial Vehicles (UAVs) are remote-controlled vehicles capable of flight and are present in a variety of environments from military operations to domestic enjoyment. These vehicles are great assets, but just as their pilot can control them remotely, cyberattacks can be executed in a similar manner. Cyber attacks on UAVs can bring a plethora of issues to physical and virtual systems. Such malfunctions are capable of giving an attacker the ability to steal data, incapacitate the UAV, or hijack the UAV. To mitigate such attacks, it is necessary to identify and patch vulnerabilities that may be maliciously exploited. In this paper, a new UAV vulnerability is explored with related UAV security practices identified for possible exploitation using large streams of data sent at specific ports. The more in-depth model involves strings of data involving FTP-specific keywords sent to the UAV's FTP port in the form of a fuzzing test and launching thousands of packets at other ports on the UAV as well. During these tests, virtual and physical systems are monitored extensively to identify specific patterns and vulnerabilities. This model is applied to a Parrot Bebop 2, which accurately portrays a UAV that had their network compromised by an attacker and portrays many lower-end UAV models for domestic use. During testings, the Parrot Bebop 2 is monitored for degradation in GPS performance, video speed, the UAV's reactivity to the pilot, motor function, and the accuracy of the UAV's sensor data. All these points of monitoring give a comprehensive view of the UAV's reaction to each individual test. In this paper, countermeasures to combat the exploitation of this vulnerability will be discussed as well as possible attacks that can branch from the fuzzing tests.