An IoT Forensic Framework based on DNA, a Hybrid Forensic Server and Confidence Value Models

  • Nancy Scheidt

Student thesis: Doctoral Thesis


The number of connected devices of the Internet of Things (IoT) rise rapidly per person and will continue to do so predicting the amount of over 25 billion connected IoT devices until 2030, which broadens the range of crime-enabled as well as dependant crimes. Additionally, due to the fast-moving nature of such developments, digital forensic, specifically, IoT forensic, struggle to keep up with this evolution effectively and sufficiently.
Some research address these issues by proposing device management methods as well as improved investigation procedures and tools, however, unfortunately, fail to address that device management can support forensic investigations. Furthermore, proposed platforms do not consider efficient national and international case investigations and only provide frameworks utilising single servers without further consideration of storage capacity and that the impact of investigators and their training, or lack thereof, may impact a court case’s outcome.
This thesis addresses the stated problems by proposing a DNA for devices, the Hybrid Forensic Server (HFS) as a server structure to be used as a dynamic platform for national and international cases as well as Confidence Values Models (CVM). Such evaluates and calculates the skills of investigators in relation to their evidence retrieval rate which affects the value of their final case report and demonstrates to other jurisdictions and investigators the confidence’s influence on the process which may affect the sharing of further evidence during the case. This research proved that letting these aspect work together can significantly improve forensic investigation for police, especially when considering
cases where evidence cannot be retrieved in a straightforward manner and the
procedure being more challenging. Demonstrated cases showed that the proposed DNA, HFS and CVM effortlessly work together within the multilayered framework, showing that proposed investigation routines work more efficiently due to less effects on the overall server storage, allowing IoT forensic to be improved greatly.
Date of Award4 Jul 2022
Original languageEnglish
Awarding Institution
  • University of Portsmouth
SupervisorMo Adda (Supervisor), Linda Yang (Supervisor) & Benjamin Yowell Yousif Aziz (Supervisor)

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