Semantic embeddings for program behaviour patterns

Alexander Chistyakov, Ekaterina Lobacheva, Arseny Kuznetsov, Alexey Romanenko

Abstract: In this paper, we propose a new feature extraction technique for program execution logs. First, we automatically extract complex patterns from a program's behaviour 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.

TL;DR: We propose a novel method of graph based representation learning for program execution logs.

Conflicts: kaspersky.com, hse.ru, edu.hse.ru, cs.msu.ru

Keywords: Deep learning, Unsupervised Learning, Applications