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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.10327 |
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| _version_ | 1866916954218954752 |
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| author | Hu, Haoyang Huang, Xun Wu, Chenyu Liu, Shiwen Lian, Zhichao Zhang, Shuangquan |
| author_facet | Hu, Haoyang Huang, Xun Wu, Chenyu Liu, Shiwen Lian, Zhichao Zhang, Shuangquan |
| contents | Intrusion detection systems (IDS) are widely used to maintain the stability of network environments, but still face restrictions in generalizability due to the heterogeneity of network traffics. In this work, we propose BERTector, a new framework of joint-dataset learning for IDS based on BERT. BERTector integrates three key components: NSS-Tokenizer for traffic-aware semantic tokenization, supervised fine-tuning with a hybrid dataset, and low-rank adaptation for efficient fine-tuning. Experiments show that BERTector achieves state-of-the-art detection accuracy, strong generalizability, and excellent robustness. BERTector achieves the highest accuracy of 99.28% on NSL-KDD and reaches the average 80% detection success rate against four perturbations. These results establish a unified and efficient solution for modern IDS in complex and dynamic network environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10327 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BERTector: An Intrusion Detection Framework Constructed via Joint-dataset Learning Based on Language Model Hu, Haoyang Huang, Xun Wu, Chenyu Liu, Shiwen Lian, Zhichao Zhang, Shuangquan Cryptography and Security Intrusion detection systems (IDS) are widely used to maintain the stability of network environments, but still face restrictions in generalizability due to the heterogeneity of network traffics. In this work, we propose BERTector, a new framework of joint-dataset learning for IDS based on BERT. BERTector integrates three key components: NSS-Tokenizer for traffic-aware semantic tokenization, supervised fine-tuning with a hybrid dataset, and low-rank adaptation for efficient fine-tuning. Experiments show that BERTector achieves state-of-the-art detection accuracy, strong generalizability, and excellent robustness. BERTector achieves the highest accuracy of 99.28% on NSL-KDD and reaches the average 80% detection success rate against four perturbations. These results establish a unified and efficient solution for modern IDS in complex and dynamic network environments. |
| title | BERTector: An Intrusion Detection Framework Constructed via Joint-dataset Learning Based on Language Model |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2508.10327 |