Abstract
The continued and rapid development of Internet of Things (IoT) devices in our modern world has brought significant changes to how society is being productive and connected. The widespread uptake and application of IoT devices in the past 10 years alone has seen how technology has advanced to aid in our daily lives and is now considered an integral factor to living. However, this advancement has come with a cost, the art of performing digital forensic investigations, particularly with IoT devices, has stagnated. Traditional methods that have been developed and applied in computer forensics over the last 20 years cannot be directly applied to the new domain of IoT forensics, mainly due to its greater complexity and being crossover with network and cloud forensics.
New forensic methods must be researched and established that can address these issues, while still maintaining the core principle of digital forensics, namely, to maintain the chain of custody throughout the investigative process. An approach still in its infancy is applying artificial intelligence (AI) to the IoT forensics domain, particularly a semantic-based ontology driven by machine learning (ML). This chapter focuses on the proposed methodologies and benefits that integrating ontologies and AI offer digital forensics investigators.
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Grojek, A.E., Sikos, L.F. (2022). Ontology-Driven Artificial Intelligence in IoT Forensics. In: Daimi, K., Francia III, G., Encinas, L.H. (eds) Breakthroughs in Digital Biometrics and Forensics. Springer, Cham. https://doi.org/10.1007/978-3-031-10706-1_12
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