  Modern malware evolves various detection avoidance techniques to bypass the
state-of-the-art detection methods. An emerging trend to deal with this issue
is the combination of image transformation and machine learning techniques to
classify and detect malware. However, existing works in this field only perform
simple image transformation methods that limit the accuracy of the detection.
In this paper, we introduce a novel approach to classify malware by using a
deep network on images transformed from binary samples. In particular, we first
develop a novel hybrid image transformation method to convert binaries into
color images that convey the binary semantics. The images are trained by a deep
convolutional neural network that later classifies the test inputs into benign
or malicious categories. Through the extensive experiments, our proposed method
surpasses all baselines and achieves 99.14% in terms of accuracy on the testing
set.
