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AI-Enabled Digital Forensic Evidence Examination

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Book cover Advances in Information and Communication (FICC 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1129))

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Abstract

Digital forensics is crucial for the prosecution of offenders in cyberspace, including nation-state actors and non-nation-state actors. The evidence discovered, verified, and associated during the evidence examination phase serves as the basis for digital forensic analysis and eventually the basis for the verdict of a judge and a jury. However, the current digital forensic evidence examination procedure usually takes a relatively long time, demands a great amount of resources, and requires great efforts from human experts. The biggest challenges in this procedure are accuracy and speed. Nevertheless, in some cases, delay is not allowed, as it may have significant impact upon a critical mission, especially a time-sensitive one. To address this challenge, this paper recommends an AI-based digital forensic evidence examination architecture that is empowered by contextual binding, machine learning, and human-machine teaming. In this approach, human experts are teamed up with artificial intelligence (AI) systems in conducting evidence examination. This approach certainly improves the efficiency and effectiveness of an investigation, thus successfully supporting missions.

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Author information

Authors and Affiliations

  1. U.S. National Defense University, Fort McNair, Washington DC, USA

    Jim Q. Chen

Authors
  1. Jim Q. Chen
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Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Chen, J.Q. (2020). AI-Enabled Digital Forensic Evidence Examination. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication. FICC 2020. Advances in Intelligent Systems and Computing, vol 1129. Springer, Cham. https://doi.org/10.1007/978-3-030-39445-5_60

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