Authors: S M Rakib Hasan, Aakar Dhakal

Published on: April 03, 2024

Impact Score: 8.0

Arxiv code: Arxiv:2404.02372

Summary

• What is new: A cost-effective system using machine-learning algorithms for detecting obfuscated malware through memory dump analysis.
• Why this is important: The increasing use of obfuscation techniques by malware authors to evade detection.
• What the research proposes: A memory dump analysis tool that employs machine-learning algorithms to identify obfuscated malware.
• Results: Effective detection of various categories of obfuscated malware, showcasing the potential of using machine-learning in cybersecurity.

Technical Details

Technological frameworks used: nan

Models used: Decision trees, ensemble methods, neural networks

Data used: CIC-MalMem-2022 dataset

Potential Impact

Cybersecurity providers, IoT device manufacturers, mobile and computer hardware companies could benefit from these insights.

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