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通过组合技术提高网络安全垃圾邮件检测的准确性:用于垃圾邮件检测的堆叠方法

Ye Tian^1,2, Xin Dai³, Zhijun Li^1,2

¹Academy of Forensic Science, Shanghai, China.

|September 3, 2025

概括

有效的垃圾邮件检测对于网络安全至关重要. 算法组合方法,特别是优化堆叠组合,显著提高垃圾邮件识别的准确性,优于单个模型和现有解决方案.

科学领域:

计算机科学
网络安全
机器学习

背景情况:

不被要求的电子邮件 (垃圾邮件) 的扩散构成了严重的网络安全威胁,并降低了用户体验.
有效的垃圾邮件检测机制对于现代数字安全基础设施至关重要.
机器学习 (ML) 为打击垃圾邮件提供了有希望的方法.

研究的目的:

评估机器学习模型用于垃圾邮件检测的性能.
提出和验证一个优化的堆叠组合框架,以加强垃圾邮件的识别.
展示集体方法比单个模型的优越性.

主要方法:

对公共垃圾邮件数据集的机器学习模型的实证分析.
开发一个堆叠集体框架,集成纯粹贝叶斯 (NBC),k-最近邻居 (k-NN),物流回归 (LR) 和XGBoost (XGBoost) 基本模型.
网格搜索交叉验证与超参数优化用于模型调整.

主要成果:

与单个模型相比,算法组合方法显示出更高的检测准确性.
优化的堆叠组件达到99.79%的高精度.
与基线模型和现有文献解决方案相比,观察到具有统计意义的改善.

结论:

优化组合方法是高级垃圾邮件检测的高效策略.
拟议的堆叠组合框架提供了一个强有力的解决方案,以加强针对电子邮件威胁的网络安全.
进一步研究组合技术可以加强数字安全和用户体验.

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