一个用于隐蔽网络结构的网络生成器
Amr Elsisy1,2, Aamir Mandviwalla1,2, Boleslaw K Szymanski1,2,3
1Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
Information sciences
|November 6, 2023
概括
研究人员开发了一种新的方法来重新连接秘密网络,创造出统计学上相似的合成网络. 这有助于识别犯罪或恐怖组织内部的稳定结构,进行分析和匿名化.
科学领域:
- 网络科学 网络科学
- 计算社会科学 计算社会科学
- 网络安全 网络安全
背景情况:
- 隐蔽网络,如犯罪或恐怖组织,由于成员试图隐活动和协会,因此使用不完整的数据运作.
- 了解这些网络的组织结构对于情报和安全分析至关重要.
- 现有的方法难以生成现实的合成网络,以捕捉隐蔽结构的复杂性.
研究的目的:
- 引入一种用于生成统计学上相似的合成秘密网络的新方法.
- 模拟隐藏网络的边缘结构和层次组织.
- 为分析网络稳定性和匿名化敏感网络数据提供工具.
主要方法:
- 一种用于隐蔽网络的新型重新布线方法,通过边缘连接标准偏差进行参数化.
- 使用多层网络建模更高层次的组织结构.
- 在最低的网络层面使用随机区块模型.
- 从原始的秘密网络数据生成众多合成网络.
主要成果:
- 生成的合成网络在统计上与自身和原始网络相似.
- 将边缘结构和层次结构一起建模对于生成现实的网络至关重要.
- 一个很小的百分比 (18%) 的合成网络结构是持续重复的,表明稳定的组织模式.
- 识别了经常重复的结构作为基础真相网络架构的强有力的候选人.
结论:
- 开发的方法有效地产生了合成隐形网络,保留了原始数据的统计特性.
- 该方法允许在隐蔽网络中识别稳定,频繁的结构,有助于分析.
- 合成网络可以用于匿名化和在开放研究环境中测试软件.
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