We proposed two new methods for visualization analysis based on N-gram features of malware. Method 1 uses space filling curves to solve the problem that the existing grayscale method cannot locate character information for interactive analysis. Method 2 visualizes the bi-gram features of malware to solve the problem that the attackers may relocate code sections or add redundant data to change the global image features of the visualized results. We designed the deep fusion networks to validate the detection and classification performances of the proposed methods

and the experimental results are very promising.