JUCS - Journal of Universal Computer Science 25(9): 1199-1218, doi: 10.3217/jucs-025-09-1199
Digital Investigation of IoT Devices in the Criminal Scene
expand article infoFrançois Bouchaud, Gilles Grimaud§, Thomas Vantroys§, Pierrick Buret|
‡ IRCGN - Forensic Science Laboratory, Pontoise, France§ University of Lille, Lille, France| C3N - National Cyber-Crime Unit, Pontoise, France
Open Access
The Internet of Things (IoT) is everywhere around us. Smart communicating objects are offering the digitalization of lives. They create new opportunities within criminal investigations. In recent years, the scientific community sought to develop a common digital framework and methodology adapted to IoT-based infrastructure. However, the difficulty in exploiting the IoT lies in the heterogeneous nature of the devices, the lack of standards and the complex architecture. Although digital forensics are considered and adopted in IoT investigations, this work only focuses on the collection. The identification phase is quite unexplored. It addresses the challenges of locating hidden devices and finding the best evidence to be collected. The matter of facts is the traditional method of digital forensics does not fully fit the IoT environment. Furthermore, the investigator can no longer consider a connected object as a single device, but as an interconnected whole one, anchored in a cross-disciplinary environment. This paper presents the methodology for identifying and classifying connected objects in search of the best evidence to be collected. It offers techniques for detecting and locating the appropriate equipment. Based on frequency mapping and interactions, it transfers the concept of "fingerprinting" into the field of crime scene. It focuses on the technical and data criteria to successfully select the relevant IoT devices. It gives a general classiffication as well as the limits of such an approach. It shows the collection of digital evidence by focusing on pertinent information from the Internet of Things.
Internet of Things, Digital Forensics Model, IoT forensics, IoT investigations, evidence1 acquisition