Advisor:

• Keerthi Dasala

Description:

Our work is focused on developing innovative techniques to identify unique device fingerprints through wireless communication protocols. The motivation behind this project lies in the need for enhanced security and authentication in wireless networks such as WiFi, where identifying devices by their unique physical properties—such as phase shifts, clock offsets, and other hardware-induced signal variations—can add a critical layer of protection. These unique signal properties akin to physically unclonable functions (PUFs), stem from the intrinsic characteristics of the device's silicon hardware, making each device's wireless signature unique and difficult to replicate.

So core idea is straightforward: given a device with a certain wireless hardware configuration X, our idea is to identify X through its wireless interactions.

During the initial phase of the project, we plan to collect, categorize and evaluate existing techniques for device fingerprinting to identify their pros and shortcomings.

As the project progresses, the team will work toward refining and developing new methods for accurate and reliable device identification.

Relevant Technologies:

• Signals and Systems
• Signal Processing
• Wireless Networking (Physical Layer)
• Machine learning
• RF and Radar systems

Prerequisites:

Students are expected to have a working understanding of WiFi protocols (e.g., IEEE 802.11), RF systems, signal propagation, and ability to use simulation tools such as MATLAB, Python, Wireshark, ns-3, and experience with SDRs (USRP) and GNU Radio.