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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Bluetooth Low Energy Device Classifier |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | In 2011, the introduction of Bluetooth Low Energy (BLE) marked a significant shift in wireless communication, paving the way for the Internet of Things (IoT) and the rise of location-based trackers. While devices like Apple's AirTag provide convenience, they pose security risks, notably the potential for malicious actors to track individuals unbeknownst to them. This work aims to address security concerns related to BLE trackers, especially considering the disparity between protections for iOS and Android users. The research focuses on creating an Android application, improving upon previous tools like HomeScout, which had limited classification capabilities. A feature based prototype was proposed and three classification models including SVM, Random Forest, and Multi-layer Perceptron were evaluated. The result was an effective classification method for BLE devices, with the Multi-Layer Perceptron model outperforming others with a 94.5\% accuracy on test data. The model was further tested on unseen device to evaluate its generalization capability, which achieved a 88\% of accuracy in with binary classification target, tracker and non-tracker. This model was integrated into the HomeScout app after resolving an identified bug in the original application. Eventually, Homescout is able to identify tracker and non-tracker device after integration. Future work entails refining the prototype, enhancing the dataset's diversity, and ensuring user privacy in public datasets. |
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