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

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.