Machine Learning in IoT and Mobile Device Forensics
Mahmoud Basharat · Advances in digital crime, forensics, and cyber terrorism book series · 2024
The increasing integration of the Internet of Things (IoT) and mobile devices in everyday life has led to significant advancements in the field of digital forensics. However, the complexity and volume of data generated by these devices pose challenges for traditional forensic methods. Machine learning (ML) has emerged as a powerful tool to address these challenges by enabling the automation of data analysis, anomaly detection, and pattern recognition in IoT and mobile device forensics. This chapter explores the role of machine learning in enhancing forensic investigations, with a focus on its application to IoT devices and mobile phones. It highlights various machine learning techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning, and examines their potential in solving complex forensic cases. The chapter also discusses the ethical and legal considerations surrounding the use of machine learning in forensics, as well as its limitations and future prospects in the evolving landscape of digital forensics.