Towards a fast off-line static malware analysis framework
| dc.contributor.author | Chikapa, Macdonald | |
| dc.contributor.author | Namanya, Anitta Patience | |
| dc.date.accessioned | 2023-05-05T17:58:03Z | |
| dc.date.available | 2023-05-05T17:58:03Z | |
| dc.date.issued | 2018 | |
| dc.description.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. | en_US |
| dc.identifier.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 | en_US |
| dc.identifier.other | 10.1109/W-FiCloud.2018.00035 | |
| dc.identifier.uri | https://nru.uncst.go.ug/handle/123456789/8641 | |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.subject | Malware | en_US |
| dc.subject | Hash | en_US |
| dc.subject | Clustering | en_US |
| dc.subject | Malware detection | en_US |
| dc.title | Towards a fast off-line static malware analysis framework | en_US |
| dc.type | Article | en_US |
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