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A Concept Forensic Methodology For The Investigation Of IoT Cyberincidents

Authors: Juan Manuel Castelo Gómez; Javier Carrillo Mondéjar; José Roldán Gómez; José Luis Martínez 0001;

A Concept Forensic Methodology For The Investigation Of IoT Cyberincidents

Abstract

Abstract The number of Internet of Things (IoT) forensic investigations has increased considerably over recent years due to the weak nature of the security measures of its devices. In order to ensure the effectiveness and completeness of their examinations, investigators rely on forensic models, frameworks and methodologies. However, given the novelty of the environment, the existing ones are not refined enough, and the conventional counterparts do not satisfy the requirements of the IoT. Consequently, further improvements are needed in order for a more suitable IoT methodology to be designed. After reviewing the proposals from the research community for the development of procedures for performing IoT investigations, this article presents a practical concept methodology for conducting IoT forensic investigations that details step by step the whole examination process from its opening to its closing. In order to test its effectiveness and feasibility, it is submitted to a theoretical, a practical and a hybrid evaluation. Firstly, by comparing its level of detail, practicality and content with the related work. Secondly, by assessing its performance in two practical scenarios that depict real-life forensic investigations and the challenges that they present. And, finally, by studying how the existing models from the research community would have behaved in these cases. After performing these three different evaluations, it can be concluded that the results achieved by the proposed methodology were satisfactory, confirmed the feasibility of the proposal and showed clear benefits compared with the related work in terms of practicality and level of detail.

Country
Spain
Keywords

Digital Forensics, Cybersecurity, Forensic Methodology, Internet of Things, IoT Forensics

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    4
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Top 10%
Average
Top 10%
Green