| Title | Disparity Analysis Between the Assembly and Byte Malware Samples with Deep Autoencoders |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Abdullah, Muhammed Amin, Yu, Yongbin, Cai, Jingye, Imrana, Yakubu, Tettey, Nartey Obed, Addo, Daniel, Sarpong, Kwabena, Agbley, Bless Lord Y., Appiah, Benjamin |
| Conference Name | 2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) |
| Keywords | Assembly file, codes, Data models, deep autoencoder, Human Behavior, information processing, Malware, malware analysis, Media, Metrics, noise reduction, privacy, pubcrawl, raw binary files, resilience, Resiliency, Training |
| Abstract | Malware attacks in the cyber world continue to increase despite the efforts of Malware analysts to combat this problem. Recently, Malware samples have been presented as binary sequences and assembly codes. However, most researchers focus only on the raw Malware sequence in their proposed solutions, ignoring that the assembly codes may contain important details that enable rapid Malware detection. In this work, we leveraged the capabilities of deep autoencoders to investigate the presence of feature disparities in the assembly and raw binary Malware samples. First, we treated the task as outliers to investigate whether the autoencoder would identify and justify features as samples from the same family. Second, we added noise to all samples and used Deep Autoencoder to reconstruct the original samples by denoising. Experiments with the Microsoft Malware dataset showed that the byte samples' features differed from the assembly code samples. |
| DOI | 10.1109/ICCWAMTIP56608.2022.10016485 |
| Citation Key | abdullah_disparity_2022 |
Disparity Analysis Between the Assembly and Byte Malware Samples with Deep Autoencoders