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Updated: Jan 16, 2026

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Analysis of Multidimensional Microscopy Data Using Cell-ACDC
Published on: November 7, 2025
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一个多维的DNS域情报数据集用于网络安全研究
Radek Hranický1, Ondřej Ondryáš1, Adam Horák1
1Faculty of Information Technology, Brno University of Technology, Božetěchova 1/2, 612 00 Brno, Czech Republic.
Data in brief
|October 6, 2025
概括
这项研究引入了用于网络安全研究的大型多源数据集,有助于网络鱼和恶意软件检测. 该数据集有助于分析互联网域名,以改进网络安全解决方案.
科学领域:
- 网络安全和网络安全网络安全.
- 数据科学和机器学习
背景情况:
- 网络威胁如网络鱼和恶意软件的复杂性和频率正在增加.
- 数据驱动的方法对于有效检测和分析这些威胁至关重要.
- 现有的数据集缺乏用于强大的网络安全研究所需的全面,多维数据.
研究的目的:
- 为网络安全研究提供一个独特的大规模数据集.
- 为了促进对互联网域名的分类和分析,以识别威胁.
- 为开发和测试先进网络安全解决方案提供丰富的资源.
主要方法:
- 从超过一百万个互联网域名收集数据.
- 来自多种来源的综合元信息:DNS,TLS,WHOIS,RDAP,IP和地理位置.
- 将数据集格式化为JSON,以实现广泛的可访问性和集成.
主要成果:
- 该数据集包含详细的标签,区分网络鱼,恶意软件和良性互联网域名.
- 它提供了域特征的全面,多维的视图.
- 数据集的数量和特征的多样性超过目前的公共资源.
结论:
- 本文所介绍的数据集是推动网络安全研究的宝贵资产.
- 它丰富的多维数据支持对网络威胁的更深入分析.
- 该数据集有助于开发更强大,更适应性的网络安全解决方案.
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