可解释的网络鱼网站检测用于安全和可持续的网络基础设施
Tanzila Kehkashan1,2, Maha Abdelhaq3, Ahmad Sami Al-Shamayleh4
1Faculty of Computing, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia.
Scientific reports
|November 25, 2025
概括
这项研究使用机器学习和Shapley添加式解释 (SHAP) 来增强网络鱼网站的检测,以识别关键特征. 随机森林模型实现了97%的准确性,提供了对网络犯罪的更易解释和更有效的解决方案.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 网络鱼攻击是一个日益增长的网络犯罪威胁,影响着各种行业.
- 传统的网络鱼检测方法 (黑名单,启发式) 是不够的和资源密集的.
- 以前的研究往往忽视了检测模型中特征选择的重要性.
研究的目的:
- 通过应用功能选择技术来改善网络鱼网站的检测.
- 为了提高模型的解释性和精度,使用Shapley添加式解释 (SHAP).
- 评估不同机器学习模型在网络鱼检测方面的有效性.
主要方法:
- 利用了11000多个URL的数据集,拥有30个用于网络鱼网站检测的功能.
- 应用特征选择技术,特别是SHAP,以增强基于URL的检测模型.
- 经过训练和测试的支持向量机 (SVM),随机森林 (RF),决策树 (DT),后勤回归 (LR) 和K-最近邻居 (KNN) 模型.
主要成果:
- 随机森林模型实现了最高准确率的97%.
- SHAP分析突出了改善检测和解释性最重要的特征.
- 使用准确度,精度,回忆和F1分数来评估表现.
结论:
- 拟议的系统为网络鱼检测提供了一个准确和可解释的解决方案.
- 使用SHAP进行特征选择可以显著提高机器学习模型的性能.
- 该研究通过改善对网络鱼攻击的防御,为更安全的数字环境做出了贡献.
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