Poster: Fingerprinting IoT Devices in Open-world Setting

Abstract

Existing fingerprinting attacks on IoT devices have primarily focused on closed-world settings and lack comprehensive open-world analysis. In this poster, we try to understand how effectively an attacker can fingerprint unseen targeted IoT devices when building a classifier using either devices manufactured by the same company or devices with similar functionality. We find that an adversary benefits when the training set contains at least one device type per company, enabling it to predict the other devices manufactured by the same company even when the device functionality might be different

Dilawer Ahmed

Phd Student

My research interests include distributed robotics, mobile computing and programmable matter.