IoT Device Forensics and Data Reduction
By Darren Quick, Kim-Kwang Raymond Choo. 2018
48 citations recorded by the source corpus.
The growth in the prevalence of the plethora of digital devices has resulted in growing volumes of disparate data, with potential relevance to criminal and civil investigations. With the increase in data volume, there is an opportunity to build greater case-related knowledge and discover evidence, with implications at all stages of the digital forensic analysis process. The growth in digital devices will potentially further contribute to the growth in big digital forensic data, with a need for practitioners to consider a wider range of data and devices that may be relevant to an investigation. A process of data reduction by selective imaging and quick analysis, coupled with automated data extraction, gives potential to undertake the analysis of the growing volume of data in a timely manner. In this paper, we outline a process of bulk digital forensic data analysis including disparate device data. We research the process with a research data corpus and apply our process to real-world data. The challenges of the growing volume of devices and data will require forensic practitioners to expand their ability to undertake research into newly developed data structures, and be able to explain this to the court, judge, jury, and investigators.
Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing
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In Information Systems
Citations in the corpus
- A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues (2020, methodology): “Advanced presentation techniques are required, especially in such cases, when the data structure has been reverse engineered by the forensics practitioners [68].”