Survey and Analysis of IoT Operating Systems: A Comparative Study on the Effectiveness and Acquisition Time of Open Source Digital Forensics Tools

TL;DR

This study evaluates the effectiveness and acquisition time of open-source digital forensics tools across various IoT operating systems, providing insights and recommendations for forensic practitioners and IoT device security.

Contribution

It offers a comprehensive comparison of open-source forensic tools on IoT devices, highlighting their performance and challenges, which is a novel contribution to IoT digital forensics.

Findings

Identifies the most efficient open-source forensic tools for IoT devices.

Provides detailed analysis of forensic task durations across IoT operating systems.

Highlights challenges in maintaining evidence integrity during IoT forensic investigations.

Abstract

The main goal of this research project is to evaluate the effectiveness and speed of open-source forensic tools for digital evidence collecting from various Internet-of-Things (IoT) devices. The project will create and configure many IoT environments, across popular IoT operating systems, and run common forensics tasks in order to accomplish this goal. To validate these forensic analysis operations, a variety of open-source forensic tools covering four standard digital forensics tasks. These tasks will be utilized across each sample IoT operating system and will have its time spent on record carefully tracked down and examined, allowing for a thorough evaluation of the effectiveness and speed for performing forensics on each type of IoT device. The research also aims to offer recommendations to IoT security experts and digital forensic practitioners about the most efficient open-source tools for forensic investigations with IoT devices while maintaining the integrity of gathered evidence and identifying challenges that exist with these new device types. The results will be shared widely and well-documented in order to provide significant contributions to the field of internet-of-things device makers and digital forensics.