Team Name
AeroPwn
Timeline
Summer 2025 – Fall 2025
Students
- Elvin Palushi – Software Engineering
- Ethan Grandin – Software Engineering
- Uriel Lujan – Software Engineering
Sponsor
Trevor Jay Bakker – Adjunct Professor, The University of Texas at Arlington, Computer Science and Engineering
Abstract
RAVEN is a drone equipped with a modular payload that targets networks that the user wants to run network penetration tests, such as a deauthentication attack, and wants to collect network data that can later be analyzed using RAVEN’s included After-Action Report desktop application. Key requirements for RAVEN include: simultaneous 2.4GHz, 11-channel Wi-Fi scanning and logging to monitor traffic, active wireless attacking capabilities, and GPS-tagged reconnaissance. The After Action Report application will map locations of networks that were captured and stored onto the micro SD card through the Google Maps integration in order to test captured traffic from different networks.
Background
RAVEN (Reconnaissance Aerial Vehicle for Exploitation and Network-mapping) is a war-flying drone that performs penetration tests and scans through various attacks on wireless networks. RAVEN also includes a desktop application that visualizes and displays operational data collected by the drone. Inspired by the likes of Kismet, Pwnagotchi, and Wi-Fi Pineapple, RAVEN features a full suite of red team capabilities.
Project Requirements
2.4GHz Wi-Fi Scanning and Logging: There are 11 Wi-Fi channels (USA) through which network traffic may be transmitted. Scanning all 11 channels enables RAVEN to cover the entire 2.4 GHz Wi-Fi spectrum.
Active Wireless Attacks: Some of the Active Wireless Attacks we have implemented included: Evil Twin, Deauthentication, Packet and Beacon flood.
GPS-Tagged Reconnaissance: As this drone is classified as “war-flying” it must be able to record the coordinates where the vulnerable network is located.
After Action Report: The After Action Report provides the user with a high-level view of all the data logged by the RAVEN, as well as where the data was collected.
Design Constraints
- Cost: Due to the components used, RAVEN must be produced within the budget allocated for our team, which is $800.
- Environmental: The After Action Report will be locked to the UTA campus to ensure testing of RAVEN is in accordance with UTA policies and procedures.
- Functionality: The drone has a small payload capacity in which a balance between weight and battery capacity must be found to maximize operational flight time.
- Interoperability: The After Action Report must be portable and reasonably easy to install on popular operating systems such as Windows, Mac, and Linux.
- Safety & Welfare: The pilot of the drone must have past experience with flying drones like RAVEN to ensure the safety and welfare of everyone involved.
Engineering Standards
- ISO/IEC 14882: For ESP32 Sketches
- ISO 21384-2: For Drone Requirements
- ISO 21384-3: For Drone Operations
- IEC 60086-4: For Drone Battery
- IEC 82079-1: For Project Documentation
System Overview
RAVEN is comprised of three distinct layers: the Capturing, Processing, and After Action Report layers. An overview of how the product will work is that the processing layer will communicate with the capturing layer to ensure network traffic is being captured. From there, the penetration tests will be initiated from the processing layer to the capturing layer in order to test a wireless network’s security. After that, the captured data will be sent to RAVEN’s onboard Pi 4. Collected data can be transferred to the after-action report layer, running on a desktop, via Micro SD. Finally, the data collected from the flight will be visually displayed to the user for further analysis and review.
Results
AeroPwn has provided a proof-of-concept by creating a platform that expands the abilities of red teams to audit networks for preemptive maintenance and security. The RAVEN prototype proved that the system has the capability to function on the finalized RAVEN product, which is the version including all components on the PCB.
Future Work
Future work for the RAVEN includes automating the network password cracking features onboard the Raspberry Pi and loading that data to the MicroSD card for viewing on the After Action Report Dashboard. Another future feature is color-coding the attack frames on the data panel within the User Interface.
Project Files
Project Charter
System Requirements Specification
Architectural Design Specification
Detailed Design Specification
Color-Coded Wiring Schematic
Poster
References
Electron. “Introduction.” Electron Documentation. Accessed November 25, 2025. https://www.electronjs.org/docs/latest/
Tshark. “Capture Pcap.” Tshark Documentation. Accessed November 25, 2025. https://tshark.dev/capture/
Node.js. “Permissions.” Node.js v16.20.2 Documentation. Accessed November 25, 2025. https://nodejs.org/download/release/latest v16.x/docs/api/permissions.html
