Description
In this talk, we will introduce the potential of deep learning, as one of the most powerful and trendy machine learning, for malware analysis. Specifically, we propose a novel android malware detection system that uses a deep convolutional neural network (CNN). Malware classification is performed based on static analysis of the raw opcode sequence from a disassembled program. Features indicative of malware are automatically learned by the network from the raw opcode sequence thus removing the need for hand-engineered malware features. The training pipeline of our proposed system is much simpler than existing n-gram based malware detection methods, as the network is trained end-to-end to jointly learn appropriate features and to perform classification, thus removing the need to explicitly enumerate millions of n-grams during training. The network design also allows the use of long n-gram like features, not computationally feasible with existing methods. Once trained, the network can be efficiently executed on a GPU, allowing a very large number of files to be scanned quickly.| Period | 07 Feb 2019 |
|---|---|
| Event title | LORCA Academic Masterclasses |
| Event type | Other |
| Degree of Recognition | National |