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High-performance network attack detection in unknown scenarios based on improved vertical model

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成果类型:
期刊论文
作者:
Shuling Hou;Gaoshang Xiao;Huiying Zhou*
通讯作者:
Huiying Zhou
作者机构:
[Shuling Hou; Gaoshang Xiao; Huiying Zhou] School of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha, China
通讯机构:
[Huiying Zhou] S
School of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha, China
语种:
英文
关键词:
Attack detection;Natural language processing;Security;Vertical domain models;Unknown scenarios dataset
期刊:
Cluster Computing
ISSN:
1386-7857
年:
2025
卷:
28
期:
1
页码:
1-16
基金类别:
This work was financially supported by the Natural Science Foundation of Hunan province (2022JJ31018).
机构署名:
本校为第一且通讯机构
摘要:
In the field of cybersecurity, most research on unknown attack detection still faces challenges such as low detection accuracy, slow detection speed, and imprecise category identification. Therefore, we propose the first combination of vertical language models with unknown scenario attack detection to predict binary and multi-class attacks. Two improved architectures based on the SecureBERT vertical model are built into our method: the fine-tuned FTSecureBert and the lightweight BLWSecureBert. The evaluation results show that our fine-tuned FTSecureBert outperforms the other comparative algori...

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