Using Machine Learning Techniques to Address IoT Forensics Challenges

While the Internet of Things $\text{IoT}$ provides many useful benefits to organisations, they present cybersecurity challenges and difficulties in conducting $\text{IoT}$ digital forensic investigations. Machine Learning (ML) technologies promise to address these challenges; however, little is still known about the challenges and ML techniques and methodologies for $\text{IoT}$ digital forensics investigations. This study aims to provide an understanding of the challenges involving $\text{IoT}$ forensics investigations in recent years and the contribution of ML techniques and frameworks in addressing them. A systematic literature review, guided by Khan's et al approach (2003) was conducted. The findings indicate that $\text{IoT}$ digital forensic investigations are impeded by the heterogeneous nature of $\text{IoT}$, the lack of privacy, and lack of standardization of the investigation process, among other factors. ML supported digital forensic technologies and methodologies provide several advantages in $\text{IoT}$ investigations compared to traditional forensic methods. However, more research is still needed to leverage the capabilities of AI and ML in digital quantum forensics, virtual reality evidence analysis, mobile-based investigations, and data collection in cloud environment.