Deep learning at the shallow end: Malware classification for non-domain experts
This work addresses malware classification for non-domain experts, offering a novel method that is incremental in its application of deep learning to this domain.
The paper tackles the problem of malware classification by introducing a deep learning approach that eliminates the need for expert domain knowledge and time-consuming signature extraction, achieving classification through a purely data-driven method for identifying complex patterns and features.
Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification. Moreover, these signatures are often limited to local, contiguous sequences within the data whilst ignoring their context in relation to each other and throughout the malware file as a whole. We present a Deep Learning based malware classification approach that requires no expert domain knowledge and is based on a purely data driven approach for complex pattern and feature identification.