  Research in the field of malware classification often relies on machine
learning models that are trained on high-level features, such as opcodes,
function calls, and control flow graphs. Extracting such features is costly,
since disassembly or code execution is generally required. In this paper, we
conduct experiments to train and evaluate machine learning models for malware
classification, based on features that can be obtained without disassembly or
execution of code. Specifically, we visualize malware samples as images and
employ image analysis techniques. In this context, we focus on two machine
learning models, namely, Convolutional Neural Networks (CNN) and Extreme
Learning Machines (ELM). Surprisingly, we find that ELMs can achieve accuracies
on par with CNNs, yet ELM training requires less than~2\%\ of the time needed
to train a comparable CNN.
