Using Machine Learning Techniques to Address IoT Forensics Challenges

While the Internet of Things <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{IoT}$</tex> provides many useful benefits to organisations, they present cybersecurity challenges and difficulties in conducting <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{IoT}$</tex> 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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{IoT}$</tex> digital forensics investigations. This study aims to provide an understanding of the challenges involving <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{IoT}$</tex> 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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{IoT}$</tex> digital forensic investigations are impeded by the heterogeneous nature of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{IoT}$</tex>, 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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{IoT}$</tex> 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.