一个网络安全NER方法基于硬和简单的标签训练数据歧视
Lin Ye1, Yue Wu1, Hongli Zhang1
1School of Cyberspace Science, Harbin Institute of Technology, 150001 Harbin, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
本研究介绍了一种网络安全命名实体识别 (NER) 方法,该方法区分硬和简单的培训数据. 通过平衡这些数据类型来实现最佳性能,从而提高网络安全中的NER准确性.
科学领域:
- 自然语言处理自然语言处理.
- 网络安全 网络安全
背景情况:
- 命名实体识别 (NER) 在一般领域取得了进展,但由于数据敏感性和缺乏公共数据集,在网络安全方面面临挑战.
- 现有的研究往往侧重于增加注释量,忽视了网络安全培训数据的固有特征.
研究的目的:
- 提出一种新的网络安全命名实体识别 (NER) 方法.
- 解决当前方法的局限性,通过区分硬标记的培训数据和容易标记的培训数据.
主要方法:
- 结合深度学习 (DL) 和基于规则的方法的混合区分器被用于将数据集分成硬和简单的样本.
- 硬数据和简单数据的比例被优化为训练.
- 对分区数据应用了一个数据增强算法.
主要成果:
- 硬样本与轻样本的比例显著影响NER性能.
- 确定了最佳的1:1比例的硬样本和轻样本.
- 拟议的方法证明了网络安全NER的整体性能得到改善.
结论:
- 平衡硬标签和简单标签数据对于提高网络安全NER至关重要.
- 开发的方法为在网络安全领域培训NER模型提供了更有效的策略.
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