Unveiling the Shadows: Leveraging Current Drone Detection Vulnerabilities to Design and Build a Stealth Drone
Author | Ahmed, Fatimaelzahraa |
Author | Qassmi, Noof |
Author | Fatima Rizvi, Syeda Warisha |
Author | Al-Ali, Adulla |
Available date | 2024-08-14T06:12:19Z |
Publication Date | 2024 |
Publication Name | 2nd International Conference on Unmanned Vehicle Systems-Oman, UVS 2024 |
Resource | Scopus |
Abstract | Drones have become a popular tool for illegal activities and attacks, causing serious threats to global security. In order to address this issue, our project aims to demonstrate the limitations of current drone detection systems by constructing a stealth drone, which is called 'Ash.'. The designed drone will be capable of operating in three different modes, which are Wi-Fi, 915 MHz radio frequency (RF) signals, and autonomous mode using a global positioning system (GPS). In addition to that, the drone will be camouflaged to evade detection by optical sensors. We are using long-range (LoRa) technology to transmit on 915 MHz. This makes it difficult to be recognized by the RF analyzer as a drone communication signal. To evade detection by optical sensors, we are camouflaging the drone by adding an air balloon envelope on top of the drone's frame. This makes it appear as a flying air balloon to the detection systems, which should confuse these systems that use computer vision and artificial intelligence. To sum up, this project illustrates the importance of detecting drones accurately and the need for anti-drone systems to adapt to new technologies and tactics. By highlighting the weaknesses of current anti-drone systems, we aim to contribute to the development of more effective technologies to protect global cyberphysical security. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | anti-drones camouflage computer vision drone detection systems drones GPS LoRa module optical sensors RF analyzers Wi-Fi |
Type | Conference Paper |
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Computer Science & Engineering [2402 items ]