Publication Date
1-1-2024
Document Type
Article
Publication Title
Journal of Computer Virology and Hacking Techniques
DOI
10.1007/s11416-024-00513-5
Abstract
A large amount of new malware is constantly being generated, which must not only be distinguished from benign samples, but also classified into malware families. For this purpose, investigating how existing malware families are developed and examining emerging families need to be explored. This paper focuses on the online processing of incoming malicious samples to assign them to existing families or, in the case of samples from new families, to cluster them. We experimented with seven prevalent malware families from the EMBER dataset, four in the training set and three additional new families in the test set. The features were extracted by static analysis of portable executable files for the Windows operating system. Based on the classification score of the multilayer perceptron, we determined which samples would be classified and which would be clustered into new malware families. We classified 97.21% of streaming data with a balanced accuracy of 95.33%. Then, we clustered the remaining data using a self-organizing map, achieving a purity from 47.61% for four clusters to 77.68% for ten clusters. These results indicate that our approach has the potential to be applied to the classification and clustering of zero-day malware into malware families.
Funding Number
SGS23/211/OHK3/3T/18
Funding Sponsor
České Vysoké Učení Technické v Praze
Keywords
Malware classification, Online clustering, Static analysis, Zero-day malware
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Computer Science
Recommended Citation
Olha Jurečková, Martin Jureček, Mark Stamp, Fabio Di Troia, and Róbert Lórencz. "Classification and online clustering of zero-day malware" Journal of Computer Virology and Hacking Techniques (2024). https://doi.org/10.1007/s11416-024-00513-5
