用机器学习检测诱人的广告和网络鱼攻击的一种混合方法
Muhammad Waqas Shaukat1, Rashid Amin2, Muhana Magboul Ali Muslam3
1Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
本研究介绍了一种机器学习模型,用于使用URL,文本和图像分析进行高级网络网络鱼检测. XGBoost在测试中实现了91%的准确性,增强了互联网用户对不断变化的威胁的安全性.
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:
- 网络鱼攻击越来越复杂,对互联网用户构成重大威胁.
- 现有的用于网络鱼检测的机器学习方法通常依赖于有限的数据集和URL特征.
研究的目的:
- 开发和评估一种基于机器学习的现代方法,用于网络网络鱼检测.
- 提出一个高效的分层分类模型,利用URL,文本和图像特征.
主要方法:
- 编译了20,000个网站URL的大数据集,每个URL提取了22个特征.
- 自然语言处理 (NLP) 技术应用于网站文本,包括图像中的文字的光学字符识别 (OCR).
- 使用支持矢量机 (SVM),XGBoost,随机森林,多层感知子,线性回归,决策树,天真贝叶斯和支持矢量分类 (SVC) 算法实现了一个分层分类模型.
主要成果:
- XGBoost算法表现出卓越的性能,在训练中达到94%的准确性,在测试中达到91%.
- 多层感知器,随机森林和决策树模型也显示出高精度 (测试中分别为91%,91%,90%).
- 使用物流回归和SVM的基于文本的分类实现了分别87%和88%的准确性.
结论:
- 提出的分层分类模型有效地检测使用URL,文本和图像特征的复杂的网络鱼网站.
- 机器学习算法,特别是XGBoost,为准确高效的网络鱼检测提供了强大的解决方案.
- 这项研究通过早期检测先进的网络鱼攻击来提高互联网用户的安全性.
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