诱的背后:深入研究PhishTank的隐藏数据
Affan Yasin1, Rubia Fatima2, Javed Ali Khan3
1School of Software, Northwestern Polytechnical University, Xian 710072, Shaanxi, China.
Data in brief
|December 28, 2023
概括
这项研究使用Python脚本从PhishTank收集了超过19万个网络鱼URL. 这些数据有助于开发更好的网络鱼过器和安全防御.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 信息安全 信息安全
背景情况:
- 网络鱼是一种普遍的社交工程攻击向量,使用欺骗性的电子邮件和伪装的URL.
- 组织网络经常成为网络鱼攻击的目标,需要强大的防御机制.
研究的目的:
- 为研究和实际应用创建一个全面的网络鱼URL数据集.
- 支持开发增强的网络安全工具和策略.
主要方法:
- 使用自定义 Python 脚本从 PhishTank 网站提取数据.
- 大规模数据集的汇总,包括超过19万个网络鱼URL.
主要成果:
- 成功编译了一大批经过验证的网络鱼URL的大量数据集.
- 该数据集为网络安全研究和开发提供了宝贵的资源.
结论:
- 收集的网络鱼URL数据集可以显著改善网络鱼过器和安全培训模型.
- 该资源使研究人员和从业人员能够加强对网络鱼攻击的防御.
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