Towards a fast off-line static malware analysis framework
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Date
2018
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
The profitability in cybercrime activity has
resulted into an exponential growth of malware numbers and
complexity. This has led to both industry and academic
research building malware research labs to allow for deeper
malware analysis so that for more efficient detection
techniques can be proposed. Extended malware study could
lead to development of more advanced malware signatures,
potentially resulting into designing of secure systems thus a
resilient cyberspace. Malware classification and clustering
based on malware families and traits is an important step in
malware analysis. This paper presents a comparative study of
file format hashes that are used in the industry is conducted in
an effort towards suggesting an approach for faster and easier
offline malware classification framework.
Description
Keywords
Malware, Hash, Clustering, Malware detection
Citation
Chikapa, M., & Namanya, A. P. (2018, August). Towards a fast off-line static malware analysis framework. In 2018 6th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW) (pp. 182-187). IEEE. DOI 10.1109/W-FiCloud.2018.00035