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Semantic representation for Windows malware detection

Published on:

18 March 2024

Primary Category:

Cryptography and Security

Paper Authors:

Peter Švec,

Štefan Balogh,

Martin Homola,

Ján Kľuka,

Tomáš Bisták


Key Details

Proposes ontology for Windows malware files

Enables explainable AI for malware detection

Provides interpretable vocabulary for results

Publishes semantically annotated dataset fragments

Shows feasibility with concept learning case study

AI generated summary

Semantic representation for Windows malware detection

The paper proposes an ontology that provides a unified semantic schema for datasets of Windows malware files. This facilitates explainable AI approaches for malware detection that produce interpretable results using the ontology's vocabulary. The authors also publish semantically annotated dataset fragments to enable reproducible experiments.

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