Name
Title | ||
|---|---|---|
An Attention Mechanism for Combination of CNN and VAE for Image-Based Malware Classification |
Abstract | ||
|---|---|---|
Currently, malware is increasing in both number and complexity dramatically. Several techniques and methodologies have been proposed to detect and neutralize malicious software. However, traditional methods based on the signatures or behaviors of malware often require considerable computational time and resources for feature engineering. Recent studies have applied machine learning to the problems of identifying and classifying malware families. Combining many state-of-the-art techniques has become popular but choosing the appropriate combination with high efficiency is still a problem. The classification performance has been significantly improved using complex neural network architectures. However, the more complex the network, the more resources it requires. This paper proposes a novel lightweight architecture by combining small Convolutional Neural Networks and advanced Variational Autoencoder, enhanced by channel and spatial attention mechanisms. We achieve overperformance and sufficient time through various experiments compared to other cutting-edge techniques using unbalanced and balanced Malimg datasets. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ACCESS.2022.3198072 | IEEE ACCESS |
Keywords | DocType | Volume |
Malware, Feature extraction, Computer architecture, Convolutional neural networks, Computational modeling, Codes, Behavioral sciences, Encoding, Information security, Channel capacity, Malware classification, variational autoencoder, channel attention, spatial attention, latent representation, information security | Journal | 10 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tuan Van Dao | 1 | 0 | 0.34 |
| Hiroshi Sato | 2 | 0 | 0.34 |
| Masao Kubo | 3 | 0 | 0.34 |