一个基于歧视性TF-IDF信念规则基础的垃圾邮件检测模型
Xiting Yang1, Wenkai Zhou1, Xiping Duan2
1School of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China.
Scientific reports
|March 4, 2026
概括
本研究介绍了一种新的垃圾邮件检测模型,使用带有信念规则基础 (BRB) 的歧视性TF-IDF. 它有效地处理有限的数据,为早期威胁检测提供准确和可解释的垃圾邮件识别.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 新型垃圾邮件迅速发展,为早期检测而缺乏标记数据.
- 目前的垃圾邮件检测模型由于依赖于大型数据集和高维特征,因此难以概括和解释.
- 当前模型中的不透明性阻碍了错误追踪,并限制了早期威胁响应.
研究的目的:
- 提出一种新的信念规则基础 (BRB) 垃圾邮件检测模型,利用歧视性TF-IDF (DTI-BRB).
- 解决垃圾邮件检测中小样本条件,概括性差,决策不透明等挑战.
- 通过可解释和准确的模型,增强早期威胁检测和响应能力.
主要方法:
- 开发了一种歧视性TF-IDF (TF-IDF) 方法,将原始文本转换为低维特征,识别标志着垃圾邮件或垃圾邮件的术语.
- 将歧视性TF-IDF功能集成到信念规则基础 (BRB) 专家系统中,以减轻组合爆炸问题.
- 在小样本条件下通过两个案例研究验证了DTI-BRB模型.
主要成果:
- 在两个不同的案例研究中,DTI-BRB模型在仅200个样本中实现了91.5%和95.5%的高精度.
- 即使使用有限的标记数据,也显示出出色的预测性能,在低数据场景中表现优于传统模型.
- BRB的基于规则的推理提供了决策的解释性,促进了错误追踪.
结论:
- 拟议的DTI-BRB模型有效地解决了数据稀缺性和垃圾邮件检测特征维度的挑战.
- 该模型为早期垃圾邮件检测提供了可行的解决方案,提供了高精度和可解释的决策.
- 这种方法增强了网络安全专家系统对不断变化的威胁的实际应用.
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