Malicious software is generated with more and more modified features of which the methods to detect malicious software use characteristics. Automatic classification of malicious software is efficient because it does not need to store all characteristic. In this paper, we propose a transferred generative adversarial network (tGAN) for automatic classification and detection of the zero-day attack. Since the GAN is unstable in training process, often resulting in generator that produces nonsensical outputs, a method to pre-train GAN with autoencoder structure is proposed. We analyze the detector, and the performance of the detector is visualized by observing the clustering pattern of malicious software using t-SNE algorithm. The proposed model gets the best performance compared with the conventional machine learning algorithms.
|Title of host publication||Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings|
|Editors||Yuanqing Li, Derong Liu, Shengli Xie, El-Sayed M. El-Alfy, Dongbin Zhao|
|Number of pages||9|
|Publication status||Published - 2017|
|Event||24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China|
Duration: 2017 Nov 14 → 2017 Nov 18
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||24th International Conference on Neural Information Processing, ICONIP 2017|
|Period||17/11/14 → 17/11/18|
Bibliographical noteFunding Information:
This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD160066BD).
© Springer International Publishing AG 2017.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)