Towards the Security of AI-Enabled UAV Anomaly Detection
Towards the Security of AI-Enabled UAV Anomaly Detection
复制标题
DOI：
10.1109/icc45041.2023.10279224
复制
发表时间：
2023-05
期刊：
影响因子：
--
通讯作者：
A. Raja;Mengjie Jia;Jiawei Yuan
中科院分区：
文献类型：
--
作者：
A. Raja;Mengjie Jia;Jiawei Yuan
近年来，越来越多的无人机（UAV）被用于执行各种军事、民用和商业任务。为了保证无人机在执行这些任务时的可靠性，异常检测在当今的无人机系统中起着重要的作用。随着人工智能硬件和算法的快速发展，利用人工智能技术进行无人机异常检测已成为一种普遍趋势。虽然现有的支持AI的无人机异常检测方案已被证明是有前途的，但它们也引发了对方案本身的额外安全问题。在本文中，我们进行了一项研究，以探索和分析最先进的人工智能无人机异常检测设计中的潜在漏洞。我们首先验证安全漏洞的存在，然后提出一种迭代攻击，可以有效地利用漏洞和绕过异常检测。我们证明了我们的攻击的有效性，通过评估它对一个国家的最先进的无人机异常检测方案，我们的攻击是成功地发射而不被发现。基于我们的研究所获得的理解，本文还讨论了增强AI支持的无人机异常检测安全性的潜在防御方向。
Unmanned aerial vehicles (UAVs) are increasingly adopted to perform various military, civilian, and commercial tasks in recent years. To assure the reliability of UAVs during these tasks, anomaly detection plays an important role in today's UAV system. With the rapid development of AI hardware and algorithms, leveraging AI techniques has become a prevalent trend for UAV anomaly detection. While existing AI-enabled UAV anomaly detection schemes have been demonstrated to be promising, they also raise additional security concerns about the schemes themselves. In this paper, we perform a study to explore and analyze the potential vulnerabilities in state-of-the-art AI-enabled UAV anomaly detection designs. We first validate the existence of security vulnerability and then propose an iterative attack that can effectively exploit the vulnerability and bypass the anomaly detection. We demonstrate the effectiveness of our attack by evaluating it on a state-of-the-art UAV anomaly detection scheme, in which our attack is successfully launched without being detected. Based on the understanding obtained from our study, this paper also discusses potential defense directions to enhance the security of AI-enabled UAV anomaly detection.