在医疗保健网络安全中用于URL威胁检测的可解释AI:使用LIME和SHAP的案例研究
Dimitrios Karapiperis1, Georgios Feretzakis1, Dimitris Kalles1
1School of Science and Technology, Hellenic Open University, Patras, Greece.
Studies in health technology and informatics
|July 1, 2025
概括
这项研究增强了医院使用机器学习与可解释性工具的URL威胁检测. 它证实了简单的URL功能,如字符数和长度,可以准确地识别恶意链接,提高安全信任.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 可解释的人工智能
背景情况:
- 对恶意URL的自动过对于医院网络安全至关重要.
- 由于数据保护法规,不透明的机器学习模型可能会降低安全团队之间的信任.
研究的目的:
- 确定是否可以为高性能机器学习URL分类器提供可解释的理由,而不会牺牲检测性能.
- 加强对医疗保健网络安全中的自动化威胁检测系统的信任.
主要方法:
- 开发了一个随机森林URL分类器,并对来自PhishTank和OpenPhish的11万个URL进行了训练.
- 模型不可思议的可解释性工具LIME和SHAP被用来解释分类器的预测.
- 使用准确性,回忆力,精度,F1分数和ROC-AUC等指标来评估性能.
主要成果:
- URL分类器实现了高性能:准确率为0.985,回忆率为1.000,精度为0.985,F1为0.992,ROC-AUC为0.970.
- LIME和SHAP的解释显示,特殊字符数,数字数和非典型的URL长度解释了超过80%的恶意预测.
- 识别的词汇线索与NIST HICP网络安全建议保持一致.
结论:
- 高回忆性URL威胁检测可以有效地与人类可读的解释相结合.
- 这种方法为改善对医疗保健网络安全工具的信任和透明度提供了概念验证.
- 建议与医疗保健网络安全专业人员进行进一步验证.
关键词:
网络安全 网络安全可解释的人工智能医疗保健 医疗保健 医疗保健 医疗保健在 LIME 时代,机器学习 机器学习这就是 SHAP SHAP 的意思.URL 过器 URL 过器在XAI,XAI就是XAI.更多相关视频
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