  In this paper, we propose a new feature extraction technique for program
execution logs. First, we automatically extract complex patterns from a
program's behavior graph. Then, we embed these patterns into a continuous space
by training an autoencoder. We evaluate the proposed features on a real-world
malicious software detection task. We also find that the embedding space
captures interpretable structures in the space of pattern parts.
