Semantic embeddings for program behavior patterns
Alexander Chistyakov, Ekaterina Lobacheva, Arseny Kuznetsov, Alexey, Romanenko

TL;DR
This paper introduces a novel feature extraction method using semantic embeddings of program behavior patterns, improving malicious software detection by capturing interpretable structures in execution logs.
Contribution
It presents a new technique combining pattern extraction from behavior graphs with autoencoder-based embeddings, enhancing malware detection capabilities.
Findings
Embedding space captures interpretable structures
Improved detection accuracy on real-world malware data
Effective extraction of complex behavior patterns
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 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.
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Software Engineering Research · Network Security and Intrusion Detection
