STATOS: A portable tool for secure malware analysis and sample acquisition in low resource environments
Malware poses a significant security threat to organisations worldwide, particularly in environments with limited resources. Static analysis has emerged as a crucial technique for gaining insights into malware, but it often requires specialised hardware and software, which can be a barrier for organisations facing financial or supply constraints. To address these challenges, this study presents a Static-Analysis Operating System (StatOS), a portable Linux derivative operating system designed for static malware analysis. StatOS can be executed from a USB device, allowing organisations to perform efficient, user-friendly, and secure malware analysis even on underpowered hardware. This study contributes a practical solution to field analysis of malware within low-resource environments, providing a model and requirement data for future developments in portable cybersecurity tools. The tool was validated through a combination of expert feedback using the Delphi method and security assessments, including Monte-Carlo simulations and Common Vulnerabilities and Exposures (CVE) evaluations. Results indicate that StatOS meets and exceeds key performance requirements, with 100% of surveyed cyber specialists agreeing on its effectiveness, and 80% indicating they would use StatOS in forensic investigations. • StatOS exceeded performance requirements, with 100% of surveyed cyber specialists agreeing. • 80% of respondents indicated they would use StatOS for forensic investigations. • All surveyed cyber specialists agreed StatOS met all identified requirements.
Authors
- Nasreen Anjum (ORCID: https://orcid.org/0000-0002-7126-2177)
- Alexios Mylonas (ORCID: https://orcid.org/0000-0001-8819-5831)
- Abu Alam (ORCID: https://orcid.org/0000-0002-5958-7905)
- Javed Ali Khan (ORCID: https://orcid.org/0000-0003-3306-1195)
- Alexander Thomas Cameron
Institutions
- University of Gloucestershire (GB)
- University of Hertfordshire (GB)
- University of Portsmouth (GB)
Publication Details
- Journal
- Array
- Published
- 2025-04-05
- DOI
- https://doi.org/10.1016/j.array.2025.100391
- Citations
- 2
- Primary Topic
- Advanced Malware Detection Techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.97