Version 1
: Received: 12 December 2023 / Approved: 13 December 2023 / Online: 14 December 2023 (04:47:06 CET)
How to cite:
Debas, E.; Alhumam, N.; Riad, K. Unveiling the Dynamic Landscape of Malware Sandboxing: A Comprehensive Review. Preprints2023, 2023121009. https://doi.org/10.20944/preprints202312.1009.v1
Debas, E.; Alhumam, N.; Riad, K. Unveiling the Dynamic Landscape of Malware Sandboxing: A Comprehensive Review. Preprints 2023, 2023121009. https://doi.org/10.20944/preprints202312.1009.v1
Debas, E.; Alhumam, N.; Riad, K. Unveiling the Dynamic Landscape of Malware Sandboxing: A Comprehensive Review. Preprints2023, 2023121009. https://doi.org/10.20944/preprints202312.1009.v1
APA Style
Debas, E., Alhumam, N., & Riad, K. (2023). Unveiling the Dynamic Landscape of Malware Sandboxing: A Comprehensive Review. Preprints. https://doi.org/10.20944/preprints202312.1009.v1
Chicago/Turabian Style
Debas, E., Norah Alhumam and Khaled Riad. 2023 "Unveiling the Dynamic Landscape of Malware Sandboxing: A Comprehensive Review" Preprints. https://doi.org/10.20944/preprints202312.1009.v1
Abstract
In contemporary times, the landscape of malware analysis has advanced into an era of sophisticated threat detection. Today's malware sandboxes not only conduct rudimentary analyses but have evolved to incorporate cutting-edge artificial intelligence and machine learning capabilities. These advancements empower them to discern subtle anomalies and recognize emerging threats with a heightened level of accuracy. Moreover, malware sandboxes have adeptly adapted to counteract evasion tactics, creating a more realistic and challenging environment for malicious entities attempting to detect and evade analysis. This paper delves into the maturation of malware sandbox technology, tracing its progression from basic analysis to the intricate realm of advanced threat hunting. At the core of this evolution is the instrumental role played by malware sandboxes in providing a secure and dynamic environment for the in-depth examination of malicious code, contributing significantly to the ongoing battle against evolving cyber threats. In addressing the ongoing challenges of evasive malware detection, the focus lies on advancing detection mechanisms, leveraging machine learning models, and evolving malware sandboxes to create adaptive environments. Future efforts should prioritize the creation of comprehensive datasets, distinguish between legitimate and malicious evasion techniques, enhance detection of unknown tactics, optimize execution environments, and enable adaptability to zero-day malware through efficient learning mechanisms, thereby fortifying cybersecurity defences against emerging threats.
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
