  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.
