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Investigating Anti-Evasion Malware Triggers Using Automated Sandbox Reconfiguration Techniques

Computer Science Research Centre, University of the West of England, Bristol BS16 1QY, UK
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J. Cybersecur. Priv. 2021, 1(1), 19-39; https://doi.org/10.3390/jcp1010003
Received: 12 October 2020 / Revised: 17 November 2020 / Accepted: 18 November 2020 / Published: 20 November 2020
Malware analysis is fundamental for defending against prevalent cyber security threats and requires a means to deploy and study behavioural software traits as more sophisticated malware is developed. Traditionally, virtual machines are used to provide an environment that is isolated from production systems so as to not cause any adverse impact on existing infrastructure. Malware developers are fully aware of this and so will often develop evasion techniques to avoid detection within sandbox environments. In this paper, we conduct an investigation of anti-evasion malware triggers for uncovering malware that may attempt to conceal itself when deployed in a traditional sandbox environment. To facilitate our investigation, we developed a tool called MORRIGU that couples together both automated and human-driven analysis for systematic testing of anti-evasion methods using dynamic sandbox reconfiguration techniques. This is further supported by visualisation methods for performing comparative analysis of system activity when malware is deployed under different sandbox configurations. Our study reveals a variety of anti-evasion traits that are shared amongst different malware families, such as sandbox “wear-and-tear”, and Reverse Turing Tests (RTT), as well as more sophisticated malware samples that require multiple anti-evasion checks to be deployed. We also perform a comparative study using Cuckoo sandbox to demonstrate the limitations of adopting only automated analysis tools, to justify the exploratory analysis provided by MORRIGU. By adopting a clearer systematic process for uncovering anti-evasion malware triggers, as supported by tools like MORRIGU, this study helps to further the research of evasive malware analysis so that we can better defend against such future attacks. View Full-Text
Keywords: malware analysis; context-aware malware; anti-evasion malware detection malware analysis; context-aware malware; anti-evasion malware detection
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Figure 1

  • Externally hosted supplementary file 1
    Link: http://www.plegg.me.uk
    Description: MORRIGU software tools and curated malware evasion dataset are both available to download from the author's home page.
MDPI and ACS Style

Mills, A.; Legg, P. Investigating Anti-Evasion Malware Triggers Using Automated Sandbox Reconfiguration Techniques. J. Cybersecur. Priv. 2021, 1, 19-39. https://doi.org/10.3390/jcp1010003

AMA Style

Mills A, Legg P. Investigating Anti-Evasion Malware Triggers Using Automated Sandbox Reconfiguration Techniques. Journal of Cybersecurity and Privacy. 2021; 1(1):19-39. https://doi.org/10.3390/jcp1010003

Chicago/Turabian Style

Mills, Alan; Legg, Phil. 2021. "Investigating Anti-Evasion Malware Triggers Using Automated Sandbox Reconfiguration Techniques" J. Cybersecur. Priv. 1, no. 1: 19-39. https://doi.org/10.3390/jcp1010003

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