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PhishNet 1.0:以optuna优化的堆叠组合与基于Boruta的特征选择,用于网络鱼URL检测.

Achin Jain1, Shakir Khan2, Kashish Koli1

  • 1Department of Information Technology, Bharati Vidyapeeth's College of Engineering, New Delhi, India.

Scientific reports
|December 6, 2025
PubMed
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这项研究使用集体学习和元启发算法来增强网络鱼检测. 优化的堆叠分类器实现了96.15%的准确性,提供了可靠的网络安全方法.

科学领域:

  • 网络安全 网络安全
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 网络鱼攻击对互联网用户和组织构成重大威胁.
  • 有效地检测网络鱼网站对于网络安全至关重要.
  • 现有的方法需要改进,以提高准确性和可靠性.

研究的目的:

  • 通过将集合学习与元启发算法集成来提高网络鱼检测率.
  • 评估各种分类器和集成技术对网络鱼URL检测的性能.
  • 使用先进的元启发算法优化集合模型的超参数.

主要方法:

  • 使用Boruta方法进行特征选择.
  • 评估分类器,包括物流回归,KNN,SVM,决策树,naive Bayes和梯度提升.
  • 整体方法的实施:软投票和堆叠.
  • 使用元启发算法 (GA,ACO,PSO,贝叶斯优化,Optuna) 的堆叠模型的超参数优化.

主要成果:

  • 梯度提升,KNN和决策树在个人分类器中表现高.
  • 用后勤回归作为最终估计器的堆叠集团模型超过了其他估计器.
  • 优化了Optuna的堆叠分类器实现了最高的准确性 (96.15%),精度 (96.45%),回忆 (96.68%) 和F1得分 (96.56%).
关键词:
波鲁塔 (Boruta) 是一个波鲁塔 (Boruta)组合学习学习 组合学习后勤回归的逻辑回归机器学习 机器学习优化优化 优化优化网络鱼检测 网络鱼检测堆叠堆叠 在堆叠堆叠

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结论:

  • 集合学习与元启发式优化相结合,显著提高了网络鱼网页检测.
  • 拟议的综合框架,PhishNet 1.0,为现实世界的网络安全提供了一种新有效的方法.
  • 该研究为网络安全应用中的网络鱼检测建立了一个可重复的基准.