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11 pages, 3836 KiB

Open AccessArticle

Analysis of a Bluetooth Traffic Dataset Obtained during University Examination Sessions

by Radu Bouaru, Adrian Peculea, Bogdan Iancu, Sorin Buzura, Emil Cebuc and Vasile Dadarlat

Data 2024, 9(4), 49; https://doi.org/10.3390/data9040049 - 30 Mar 2024

Viewed by 1898

In academic environments, students take exams simultaneously in campus examination classrooms. Due to recent advancements in technology, examination rooms are flooded with Bluetooth data traffic generated by personal devices (smartphones, smartwatches, etc.). The work presented in this article proposes a method for collecting Bluetooth traffic in an academic examination setting. The desired data were collected during several examination sessions using an Ubertooth One device, and then an in-depth post-processing analysis was performed on the collected dataset. The devices generating traffic were precisely located within the examination room, and areas with heightened data traffic were highlighted. Additionally, another goal of the current research was to provide a unique type of dataset to the academic community, facilitating its utilization in further research endeavors. Full article

(This article belongs to the Section Information Systems and Data Management)

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12 pages, 614 KiB

Open AccessArticle

Revisiting Adaptive Frequency Hopping Map Prediction in Bluetooth with Machine Learning Classifiers

by Janggoon Lee, Chanhee Park and Heejun Roh

Energies 2021, 14(4), 928; https://doi.org/10.3390/en14040928 - 10 Feb 2021

Cited by 3 | Viewed by 3477

Abstract

Thanks to the frequency hopping nature of Bluetooth, sniffing Bluetooth traffic with low-cost devices has been considered as a challenging problem. To this end, BlueEar, a state-of-the-art low-cost sniffing system with two Bluetooth radios proposes a set of novel machine learning-based subchannel classification techniques for adaptive frequency hopping (AFH) map prediction by collecting packet statistics and spectrum sensing. However, there is no explicit evaluation results on the accuracy of BlueEar’s AFH map prediction. To this end, in this paper, we revisit the spectrum sensing-based classifier, one of the subchannel classification techniques in BlueEar. At first, we build an independent implementation of the spectrum sensing-based classifier with one Ubertooth sniffing radio. Using the implementation, we conduct a subchannel classification experiment with several machine learning classifiers where spectrum features are utilized. Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps.Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps. Full article

(This article belongs to the Special Issue Energy-Efficient AI-Empowered Communication Networks)

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29 pages, 10715 KiB

Open AccessEditor’s ChoiceArticle

A Comprehensive Review of RFID and Bluetooth Security: Practical Analysis

by Santiago Figueroa Lorenzo, Javier Añorga Benito, Pablo García Cardarelli, Jon Alberdi Garaia and Saioa Arrizabalaga Juaristi

Technologies 2019, 7(1), 15; https://doi.org/10.3390/technologies7010015 - 24 Jan 2019

Cited by 24 | Viewed by 15079

Abstract

The Internet of Things (IoT) provides the ability to digitize physical objects into virtual data, thanks to the integration of hardware (e.g., sensors, actuators) and network communications for collecting and exchanging data. In this digitization process, however, security challenges need to be taken into account in order to prevent information availability, integrity, and confidentiality from being compromised. In this paper, security challenges of two broadly used technologies, RFID (Radio Frequency Identification) and Bluetooth, are analyzed. First, a review of the main vulnerabilities, security risk, and threats affecting both technologies are carried out. Then, open hardware and open source tools like: Proxmark3 and Ubertooth as well as BtleJuice and Bleah are used as part of the practical analysis. Lastly, risk mitigation and counter measures are proposed. Full article

(This article belongs to the Special Issue Technology Advances on IoT Learning and Teaching)

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