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Fast and Efficient Malware Detection with Joint Static and Dynamic Features Through Transfer Learning. (arXiv:2211.13860v1 [cs.CR])
Nov. 28, 2022, 2:10 a.m. | Mao V. Ngo, Tram Truong-Huu, Dima Rabadi, Jia Yi Loo, Sin G. Teo
cs.CR updates on arXiv.org arxiv.org
In malware detection, dynamic analysis extracts the runtime behavior of
malware samples in a controlled environment and static analysis extracts
features using reverse engineering tools. While the former faces the challenges
of anti-virtualization and evasive behavior of malware samples, the latter
faces the challenges of code obfuscation. To tackle these drawbacks, prior
works proposed to develop detection models by aggregating dynamic and static
features, thus leveraging the advantages of both approaches. However, simply
concatenating dynamic and static features raises an …
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