%0 Journal Article %1 SAIDIAFASCI2022103062 %A Saidia Fascí, Lara %A Fisichella, Marco %A Lax, Gianluca %A Qian, Chenyi %D 2022 %J Computers & Security %K %P 103062 %R https://doi.org/10.1016/j.cose.2022.103062 %T Disarming Visualization-based Approaches in Malware Detection Systems %U https://www.sciencedirect.com/science/article/pii/S0167404822004540 %X Visualization-based approaches have recently been used in conjunction with signature-based techniques to detect variants of malware files. Indeed, it is sufficient to modify some byte of executable files to modify the signature and, thus, to elude a signature-based detector. In this paper, we design a GAN-based architecture that allows an attacker to generate variants of a malware in which the malware patterns found by visualization-based approaches are hidden, thus producing a new version of the malware that is not detected by both signature-based and visualization-based techniques. The experiments carried out on a well-known malware dataset show a success rate of 100% in generating new variants of malware files that are not detected from the state-of-the-art visualization-based technique.

@article{SAIDIAFASCI2022103062, abstract = {Visualization-based approaches have recently been used in conjunction with signature-based techniques to detect variants of malware files. Indeed, it is sufficient to modify some byte of executable files to modify the signature and, thus, to elude a signature-based detector. In this paper, we design a GAN-based architecture that allows an attacker to generate variants of a malware in which the malware patterns found by visualization-based approaches are hidden, thus producing a new version of the malware that is not detected by both signature-based and visualization-based techniques. The experiments carried out on a well-known malware dataset show a success rate of 100% in generating new variants of malware files that are not detected from the state-of-the-art visualization-based technique.}, added-at = {2022-12-14T12:00:51.000+0100}, author = {{Saidia Fascí}, Lara and Fisichella, Marco and Lax, Gianluca and Qian, Chenyi}, biburl = {https://www.bibsonomy.org/bibtex/28d3e4a81ec4d0f24b0012fead7c44e31/mfisichella}, doi = {https://doi.org/10.1016/j.cose.2022.103062}, interhash = {16c0e8244346f6acbe7c0f39e8ec3af5}, intrahash = {8d3e4a81ec4d0f24b0012fead7c44e31}, issn = {0167-4048}, journal = {Computers & Security}, keywords = {}, pages = 103062, timestamp = {2022-12-14T12:00:51.000+0100}, title = {Disarming Visualization-based Approaches in Malware Detection Systems}, url = {https://www.sciencedirect.com/science/article/pii/S0167404822004540}, year = 2022 }