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Dawson, L and Akinbi, A (2021) Challenges and opportunities for wearable IoT forensics: TomTom Spark 3 as a case study. Forensic Science International: Reports. ISSN 2665-9107
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Akinbi_Dawson_TomTom Spark 3 as a case study_FSIR.pdf - Accepted Version Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
Wearable IoT devices like fitness trackers and smartwatches continue to create opportunities and challenges for forensic investigators in the acquisition and analysis of evidential artefacts in scenarios where such devices are a witness to a crime. However, current commercial and traditional forensic tools available to forensic investigators fall short of conducting device extraction and analysis of forensic artefacts from many IoT devices due to their heterogeneous nature. In this paper, we conduct a comprehensive forensic analysis and show artefacts of forensic value from the physical TomTom Spark 3 GPS fitness smartwatch, its companion app installed on an Android smartphone, and Bluetooth event logs located in the app’s metadata. Our forensic methodology and analysis involved the combination and use of a non-forensic tool, a commercial forensic tool, and a non-forensic manufacturer-independent analysis platform tool specifically designed for endurance athletes to identify, extract, analyze, and reconstruct user activity data in an investigative scenario.
We show forensic metadata associated with the device information, past user activities, and audio files from the physical smartwatch. We recovered data associated with past user activities stored in proprietary activity files and databases maintained by the app on an Android smartphone. From the event logs, we show when user activity was synced with the app and uploaded to the device cloud storage. The results from our work provide vital references for forensic investigators to aid criminal investigations, highlight limitations of current forensic tools, and for developers of forensic tools an incentive into developing forensic software applications and tools that can decode all relevant data generated by wearable IoT devices.
| Item Type: | Article |
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| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Elsevier BV |
| Date Deposited: | 26 Apr 2021 09:35 |
| Last Modified: | 04 Sep 2021 05:33 |
| DOI or ID number: | 10.1016/j.fsir.2021.100198 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/14857 |
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