Moreover, the approach presented here is flexible enough to build dynamic profiles which evolve as more information about suspects becomes available. In particular, we exploit the strong relationship between users and their devices. This methodology considers, for the first time, the importance of IoT devices in the analysis of a cybercrime scene. This paper works precisely in this direction by proposing a new methodology for defining advanced cybersecurity profiles. Consequently, more research is necessary to unleash the full potential of cybersecurity profiles.
Unfortunately, even though these data are readily available there is still no way to automate the process of gathering these data and linking them to users. With data available from myriads of IoT devices, the complexity of the solutions increases but having so much data can be exploited to generate much more complete cybersecurity profiles, which in turn leads to a better understanding of the whole context of a digital investigation. Clearly, this poses new challenges but also offers new opportunities. Note that in 2019 there are 7 billion internet-connected devices worldwide, according to IoT Analytics .
However, as the number of devices continues to grow with the development of the Internet of Things (IoT) more data is available for interpretation. To facilitate understanding of how different actors are involved in a cybercrime, data related to individuals can be presented in the form of cybersecurity profiles. Once these data have been processed, the information deduced from them must be presented to different experts, who may or may not be technicians. Cybersecurity tools usually depend on huge amounts of data that must be processed in real time.
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