![]() ![]() ![]() And second, we have applied a state of art manifold learning scheme named UMAP to improve the detection of unknown malware files through binary classification. In contrast to the conventional approaches followed by static and dynamic methods existing in the literature, we aimed to obtain and use memory data to reveal visual patterns that can be classified by employing computer vision and machine learning methods in a multi-class open-set recognition regime. First, we present a novel approach to recognize malware by capturing the memory dump of suspicious processes which can be represented as a RGB image. Our contribution in this study is two-folded. ![]() Moreover, existing static malware detection methods in literature often fail to detect sophisticated malware utilizing various obfuscation and encryption techniques. In particular, according to the recent reports, the new type of fileless malware infect the victims’ devices without a persistent trace (i.e. The everlasting increase in usage of information systems and online services have triggered the birth of the new type of malware which are more dangerous and hard to detect. ![]()
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