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Current malware detection and classification approaches generally rely ontime consuming and knowledge intensive processes to extract patterns(signatures) and behaviors from malware, which are then used foridentification. Moreover, these signatures are often limited to local,contiguous sequences within the data whilst ignoring their context in relationto each other and throughout the malware file as a whole. We present a DeepLearning based malware classification approach that requires no expert domainknowledge and is based on a purely data driven approach for complex pattern andfeature identification.

Deep learning at the shallow end: Malware classification for non-domain experts