How do Transformer-based Architectures Compare to LSTM Networks for Sequence-based Malware Classification in Terms of Accuracy, Robustness to Obfuscation, and Computational Efficiency?

Authors

• Joatan Silva Sampaio Neto, Metropolitan State UniversityFollow
• Is-haq Yusuf, Metropolitan State UniversityFollow
• Sterling Gjesvold, Metropolitan State UniversityFollow

Department

Computer Science and Cybersecurity

Document Type

Poster

Publication Date

Spring 4-9-2026

Comments

Spring 2026: Student Research Conference

Distinguished Presenter Award: Sterling Gjesvold

Recommended Citation

Silva Sampaio Neto, J. Yusuf, I. & Gjesvold, S. (2026, April 9). How do transformer-based architectures compare to LSTM networks for sequence-based malware classification in terms of accuracy, robustness to obfuscation, and computational efficiency? [Poster presentation]. Student Research Conference Spring 2026, Saint Paul, MN, United States. https://metroworks.metrostate.edu/student-scholarship/34

Creative Commons License

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