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Malware Evolution and Detection Based on the Variable Precision Rough Set Model

Abstract : To offer innovative malware evolution techniques, it is appealing to integrate approaches that handle imperfect data and knowledge. In fact, malware writers tend to target some precise features within the app's code to camouflage the malicious content. Those features may sometimes present conflictual information about the true nature of the content of the app (malicious/benign). In this paper, we show how the Variable Precision Rough Set (VPRS) model can be combined with optimization techniques, in particular Bilevel-Optimization-Problems (BLOPs), in order to establish a detection model capable of following the crazy race of malware evolution initiated among malware-developers. We propose a new malware detection technique, based on such hybridization, named Variable Precision Rough set Malware Detection (ProRSDet), that offers robust detection rules capable of revealing the new nature of a given app. ProRSDet attains encouraging results when tested against various state-of-the-art malware detection systems using common evaluation metrics.
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Contributor : Zaineb Chelly Dagdia Connect in order to contact the contributor
Submitted on : Wednesday, July 13, 2022 - 11:32:00 PM
Last modification on : Saturday, July 23, 2022 - 3:29:59 AM


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  • HAL Id : hal-03723132, version 1


Manel Jerbi, Zaineb Chelly Dagdia, Slim Bechikh, Lamjed Ben Said. Malware Evolution and Detection Based on the Variable Precision Rough Set Model. 17th CONFERENCE ON COMPUTER SCIENCE AND INTELLIGENCE SYSTEMS, Sep 2022, Sofia, Bulgaria. ⟨hal-03723132⟩



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