| Title | An Effective Ensemble Deep Learning Framework for Malware Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Sang, Dinh Viet, Cuong, Dang Manh, Cuong, Le Tran Bao |
| Conference Name | Proceedings of the Ninth International Symposium on Information and Communication Technology |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-6539-0 |
| Keywords | composability, Ensemble Method, malware detection, Metrics, pubcrawl, ransomware, Residual Convolutional Neural Network, Resiliency |
| Abstract | Malware (or malicious software) is any program or file that brings harm to a computer system. Malware includes computer viruses, worms, trojan horses, rootkit, adware, ransomware and spyware. Due to the explosive growth in number and variety of malware, the demand of improving automatic malware detection has increased. Machine learning approaches are a natural choice to deal with this problem since they can automatically discover hidden patterns in large-scale datasets to distinguish malware from benign. In this paper, we propose different deep neural network architectures from simple to advanced ones. We then fuse hand-crafted and deep features, and combine all models together to make an overall effective ensemble framework for malware detection. The experiment results demonstrate the efficiency of our proposed method, which is capable to detect malware with accuracy of 96.24% on our large real-life dataset. |
| URL | http://doi.acm.org/10.1145/3287921.3287971 |
| DOI | 10.1145/3287921.3287971 |
| Citation Key | sang_effective_2018 |
An Effective Ensemble Deep Learning Framework for Malware Detection