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Digital forensic analysis of intelligent and smart IoT devices

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Abstract

AI is combined with various devices to provide improved performance. IoT devices combined with AI are called smart IoT. Smart IoT devices can be controlled using wearable devices. Wearable devices such as smartwatches and smartbands generate personal information through sensors to provide a range of services to users. As the generated data are preserved in the storage of the wearable device, getting access to these data from the device can prove useful in criminal investigations. We, therefore, propose a forensic model based on direct connections using wireless or interfaces beyond indirect forensics for wearable devices. The forensic model was derived based on the ecosystem of wearable devices and was divided into logical and physical forensic methods. To confirm the applicability of the forensic model, we applied it to wearable devices from Samsung, Apple, and Garmin. Our results demonstrate that the proposed forensic model can be successfully used to derive artifacts.

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2018R1D1A1B07043349). This research was supported by Energy Cloud R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (NRF-2019M3F2A1073385)

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Authors and Affiliations

  1. Department of AI Convergence network, Ajou University, Suwon, Korea

    Minju Kim, Yeonghun Shin & Taeshik Shon

  2. Department of Computer Engineering, Ajou University, Suwon, Korea

    Wooyeon Jo

  3. Department of Cyber Security, Ajou University, Suwon, Korea

    Taeshik Shon

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  1. Minju Kim
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  2. Yeonghun Shin
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Correspondence to Taeshik Shon.

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The datasets generated during and/or analysed during the current study are not available due to the privacy, so data sharing is not applicable.

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Kim, M., Shin, Y., Jo, W. et al. Digital forensic analysis of intelligent and smart IoT devices. J Supercomput 79, 973–997 (2023). https://doi.org/10.1007/s11227-022-04639-5

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  • DOI: https://doi.org/10.1007/s11227-022-04639-5

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