| dc.contributor | 資管系 | - |
| dc.creator (作者) | 蕭舜文; 謝東睿 | - |
| dc.creator (作者) | Hsiao, Shun-Wen;Hsieh, Tung-Jui;Yao, Yun-Cheng | - |
| dc.date (日期) | 2024-12 | - |
| dc.date.accessioned | 12-Mar-2025 10:22:08 (UTC+8) | - |
| dc.date.available | 12-Mar-2025 10:22:08 (UTC+8) | - |
| dc.date.issued (上傳時間) | 12-Mar-2025 10:22:08 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/156150 | - |
| dc.description.abstract (摘要) | This study presents a novel approach to enhance Large Language Models’ (LLMs) capabilities in cybersecurity analysis through two main methods. First, we propose a Structure-guided Enhancement Network that leverages Finite State Machine (FSM) representations to guide the attention mechanism in processing API call sequences for malware classification. The guided-attention mechanism integrates structural FSM features to enhance the attention computation on API sequences. Second, we develop a Multi-modal Fusion System that aligns assembly code and API call sequences from the same malware through cross-attention mechanisms, creating a shared latent space for improved context generation. Our research demonstrates how structural guidance and multi-modal fusion can enhance LLMs’ performance in cybersecurity-specific tasks. | - |
| dc.format.extent | 114 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Proceeding of IEEE International Conference on Big Data, IEEE, pp.8671-8673 | - |
| dc.title (題名) | Knowledge Injection of Structural CyberSecurity Concept into Large Language Models | - |
| dc.type (資料類型) | conference | - |
| dc.identifier.doi (DOI) | 10.1109/BigData62323.2024.10825610 | - |
| dc.doi.uri (DOI) | https://doi.org/10.1109/BigData62323.2024.10825610 | - |
