Contact Us | Language: čeština English
Title: | A perfect knife—Bulk decompilation and preprocessing tool | ||||||||||
Author: | Dorotík, Ladislav; Kincl, Jan; Oulehla, Milan; Šenkeřík, Roman; Komínková Oplatková, Zuzana | ||||||||||
Document type: | Conference paper (English) | ||||||||||
Source document: | Lecture Notes in Electrical Engineering. 2024, vol. 1081, p. 153-164 | ||||||||||
ISSN: | 1876-1100 (Sherpa/RoMEO, JCR) | ||||||||||
Journal Impact
This chart shows the development of journal-level impact metrics in time
|
|||||||||||
ISBN: | 978-981998702-3 | ||||||||||
DOI: | https://doi.org/10.1007/978-981-99-8703-0_13 | ||||||||||
Abstract: | Due to its vast popularity, the Android operating system has quickly become the main target of mobile malware creators. Consequently, there is a huge need for intensive efforts in the research of Android malware applications to identify malware features and thus be able to create an efficient method for malware detection. One of the main issues related to malware research is obtaining reliable datasets. Malware datasets could be obtained from various sources. Not only that they can tally up to thousands of individual applications, but also can contain damaged, corrupted, or otherwise invalid files. A large number of applications in a dataset is nearly impossible to be processed manually, invalid files can also jeopardize the process of analysis. For the results to be reproducible there needs to be a way how to specify which dataset was used and that it contained only valid files. This work introduces a tool, Perfect Knife, created by our research team for automatised decompilation, dataset validation, unification, and preparation for further research purposes. | ||||||||||
Full text: | https://link.springer.com/chapter/10.1007/978-981-99-8703-0_13 | ||||||||||
Show full item record |
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |