Machine Learning in IoT and Mobile Device Forensics

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Author(s): Mahmoud Basharat (Capitol Technology University, USA)
Copyright: 2025
Pages: 32
Source title: Digital Forensics in the Age of AI
Source Author(s)/Editor(s): Marwan Omar (Illinois Institute of Technology, USA)and Hewa Majeed Zangana (Duhok Polytechnic University, Iraq)
DOI: 10.4018/979-8-3373-0857-9.ch005

Keywords: Artificial Intelligence / Computer Science & IT / Criminal Science and Forensics / Information Science Reference

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Abstract

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.

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