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Design tactics for tailoring transformer architectures to cybersecurity challenges
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s10586-024-04355-0.pdf
Date
2024-04-24
Author
Avcı, Çiğdem
Tekinerdogan, Bedir
Catal, Cagatay
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In the rapidly evolving landscape of cyber threats, effective defense strategies are crucial for safeguarding sensitive information and critical systems. Deep learning methods, notably the Transformer architecture, have shown immense potential in addressing cybersecurity challenges. However, customizing, and adapting Transformer architectures for cybersecurity applications presents a challenge, demanding the utilization of effective strategies to achieve optimal performance. This study presents a comprehensive analysis of design tactics employed in tailoring Transformer architectures specifically for cybersecurity problems. Design tactics, defined as strategic solutions to architectural challenges based on well-justified design decisions, are explored in-depth within the context of cybersecurity. By examining the modifications and adaptations made to the original Transformer architecture, this study unveils the design decisions and strategies crucial for successful implementation in diverse cybersecurity domains. The findings emphasize the significance of aligning design tactics with the unique business requirements and quality factors of each specific application domain. This study contributes valuable insights into the utilization of design tactics for customizing Transformer architectures in cybersecurity, paving the way for enhanced defense strategies against the dynamic and evolving nature of cyber threats.
URI
https://hdl.handle.net/11511/109899
Journal
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
DOI
https://doi.org/10.1007/s10586-024-04355-0
Collections
Department of Computer Engineering, Article
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BibTeX
Ç. Avcı, B. Tekinerdogan, and C. Catal, “Design tactics for tailoring transformer architectures to cybersecurity challenges,”
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
, pp. 0–0, 2024, Accessed: 00, 2024. [Online]. Available: https://hdl.handle.net/11511/109899.