Device Type Classification of Internet of Things Devices on Low-End Dedicated Hardware Devices

Master thesis (2022)

Authors

J.M. Thiessen Electrical Engineering, Mathematics and Computer Science

Contributors

A. Zarras Cyber Security - EEMCS (mentor)
G. Smaragdakis Cyber Security - EEMCS (coach)
Ranga Rao Venkatesha Prasad Embedded Systems - EEMCS (coach)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Sven Thiessen
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Published Date

12-07-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The uprise of the Internet of Things (IoT) has been a hot topic for several years. While many see these devices and think they bring ease to their lives, it is far from reality. Researchers found many privacy and security problems within these devices in the last years. The popularity of these devices causes many users to bring a device into their home that could potentially infringe on privacy if not configured correctly. The first step to helping these home users secure their network starts by being able to autonomously detect the type of IoT devices connected to their network. With this information, firewall rules could enforce specific device behavior or provide the user with extra information on the type of the device and the risks it brings. It could inspire a new generation of home security devices focussing on securing IoT devices from a network perspective.

This thesis will introduce a new machine learning model to detect the type of unseen IoT device. Our work aims to classify devices into five categories, one more than the current state-of-the-art. Furthermore, we verify the model extensively on a large set of devices, with 74 measurements using Leave-One-Group-Out (LOGO) cross-validation. LOGO increases our testing set significantly in comparison with other works. Finally, LOGO will ensure that the training and test set was not handcrafted to obtain the highest possible accuracy, introducing fairness into our model design. The accuracy reached by our model is 73%, showing that Device Type Classification on unseen devices is feasible.