A REVIEW OF IOS MOBILE DEVICES FORENSIC AND INVESTIGATION FRAMEWORK INTEGRATED WITH MACHINE LEARNING

Abstract

The review covers key components of iOS forensics, including data acquisition, data analysis, and evidence interpretation, highlighting how machine learning algorithms can automate and optimize these processes. The paper also discusses the ethical and legal implications of deploying machine learning in forensic contexts, emphasizing the need for transparency, accountability, and privacy preservation. In recent years, the exponential growth of mobile devices, particularly iOS devices, has posed significant challenges to digital forensic investigators. The sheer volume of data and complexity of data stored on these devices require innovative approaches to efficiently extract, analyze, and interpret digital evidence. This review aims to provide a comprehensive overview of the integration of ML approaches with iOS mobile devices’ forensic and investigation framework. As mobile devices continue to play an increasingly central role in our daily lives, they have become a critical source of evidence in digital forensic investigations. Among these devices iOS-based mobile devices pose unique challenges due to their closed ecosystem and strong security measures.