使用机器学习对恶意URL进行分类
Shayan Abad1, Hassan Gholamy1, Mohammad Aslani1
1Department of Computer and Geo-Spatial Sciences, University of Gävle, 801 76 Gävle, Sweden.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
这项研究通过开发机器学习模型来提高网络安全,以有效检测恶意URL. 随机森林和支向量机器表现出强的性能,实例选择方法显著提高了模型效率.
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:
- 每天创建众多网站需要强大的方法来识别恶意URL,以保护用户数据.
- 不充分的网络安全措施有风险通过受损的网站危害敏感用户信息.
研究的目的:
- 开发和评估用于有效识别和分类恶意URL的机器学习模型.
- 通过提高URL威胁检测的准确性和效率来增强网络安全.
主要方法:
- 使用的机器学习算法:支持矢量机器 (SVM),随机森林 (RF),决策树 (DT) 和K-最近邻居 (KNN).
- 包含贝叶斯优化,用于准确的URL分类.
- 采用实例选择方法,包括基于局部敏感哈希的数据减少 (DRLSH),基于局部敏感哈希的边界点提取 (BPLSH) 和随机选择,以提高计算效率.
主要成果:
- 随机森林 (RF) 以高精度,回忆和F1分数表现出卓越的性能.
- 支持矢量机器 (SVM) 提供了竞争力的结果,但需要更长的培训时间.
- 实例选择方法显著影响了模型性能,突出了它们在分类管道中的重要性.
结论:
- 机器学习模型,特别是RF和SVM,是恶意URL分类的有效工具.
- 实例选择是优化这些网络安全模型效率和性能的一个关键组成部分.
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