Weaponized IoT: A Comprehensive Comparative Forensic Analysis of Hacker Raspberry Pi and PC Kali Linux Machine

The proliferation of Internet of Things (IoT) devices presents significant challenges for cybersecurity and digital forensics, particularly as these devices have become increasingly weaponised for malicious activities. This research focuses on the forensic analysis capabilities of Raspberry Pi devices configured with Kali Linux, comparing their forensic capabilities to conventional PC-based forensic investigations. The study identifies key gaps in existing IoT forensic methodologies, including limited tool compatibility, constrained data retention, and difficulties in live memory analysis due to architectural differences. The research employs a testbed-based approach to simulate cyberattacks on both platforms, capturing and analysing forensic artefacts such as system logs, memory dumps, and network traffic. The research findings reveal that while traditional PCs offer extensive forensic capabilities due to superior storage, tool support, and system logging, Raspberry Pi devices present significant forensic challenges, primarily due to their ARM architecture and limited forensic readiness. The study emphasises the need for specialised forensic tools tailored to IoT environments and suggests best practices to enhance forensic investigation capabilities in weaponised IoT scenarios. This research contributes to the field by bridging the gap between theoretical frameworks and real-world forensic investigations, offering insights into the evolving landscape of IoT forensics and its implications for digital evidence collection, analysis, and forensic readiness.

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Institutions

Publication Details

Journal
IoT
Published
2025-03-07
DOI
https://doi.org/10.3390/iot6010018
Citations
7
Primary Topic
Digital and Cyber Forensics
Type
article
Field-Weighted Citation Impact
22.72
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article

Weaponized IoT: A Comprehensive Comparative Forensic Analysis of Hacker Raspberry Pi and PC Kali Linux Machine

Dipo Dunsin, Mohamed Chahine Ghanem, Wiktor Sowinski-Mydlarz, Eduardo Almeida Palmieri et al.
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IoT
Digital and Cyber Forensics
22.72
article

Weaponized IoT: A Comprehensive Comparative Forensic Analysis of Hacker Raspberry Pi and PC Kali Linux Machine

Dipo Dunsin, Mohamed Chahine Ghanem, Wiktor Sowinski-Mydlarz, Eduardo Almeida Palmieri, Sahar Al-Sudani
article en
7 citations

Abstract

The proliferation of Internet of Things (IoT) devices presents significant challenges for cybersecurity and digital forensics, particularly as these devices have become increasingly weaponised for malicious activities. This research focuses on the forensic analysis capabilities of Raspberry Pi devices configured with Kali Linux, comparing their forensic capabilities to conventional PC-based forensic investigations. The study identifies key gaps in existing IoT forensic methodologies, including limited tool compatibility, constrained data retention, and difficulties in live memory analysis due to architectural differences. The research employs a testbed-based approach to simulate cyberattacks on both platforms, capturing and analysing forensic artefacts such as system logs, memory dumps, and network traffic. The research findings reveal that while traditional PCs offer extensive forensic capabilities due to superior storage, tool support, and system logging, Raspberry Pi devices present significant forensic challenges, primarily due to their ARM architecture and limited forensic readiness. The study emphasises the need for specialised forensic tools tailored to IoT environments and suggests best practices to enhance forensic investigation capabilities in weaponised IoT scenarios. This research contributes to the field by bridging the gap between theoretical frameworks and real-world forensic investigations, offering insights into the evolving landscape of IoT forensics and its implications for digital evidence collection, analysis, and forensic readiness.

IoTVol. 6(1)
London Metropolitan University (GB), University of Liverpool (GB)
Openalex Percentile: Top 1%
Digital and Cyber Forensics
22.72
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