• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

暗网的结构与弹性建模

Modeling structure and resilience of the dark network.

作者信息

De Domenico Manlio, Arenas Alex

机构信息

Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain.

出版信息

Phys Rev E. 2017 Feb;95(2-1):022313. doi: 10.1103/PhysRevE.95.022313. Epub 2017 Feb 27.

DOI:10.1103/PhysRevE.95.022313
PMID:28297942
Abstract

While the statistical and resilience properties of the Internet are no longer changing significantly across time, the Darknet, a network devoted to keep anonymous its traffic, still experiences rapid changes to improve the security of its users. Here we study the structure of the Darknet and find that its topology is rather peculiar, being characterized by a nonhomogeneous distribution of connections, typical of scale-free networks; very short path lengths and high clustering, typical of small-world networks; and lack of a core of highly connected nodes. We propose a model to reproduce such features, demonstrating that the mechanisms used to improve cybersecurity are responsible for the observed topology. Unexpectedly, we reveal that its peculiar structure makes the Darknet much more resilient than the Internet (used as a benchmark for comparison at a descriptive level) to random failures, targeted attacks, and cascade failures, as a result of adaptive changes in response to the attempts of dismantling the network across time.

摘要

虽然互联网的统计特性和弹性属性随时间变化不再显著,但致力于保持流量匿名的暗网仍在经历快速变化以提高其用户的安全性。在此,我们研究暗网的结构,发现其拓扑结构相当奇特,具有连接分布不均匀的特点,这是无标度网络的典型特征;路径长度非常短且聚类系数高,这是小世界网络的典型特征;并且缺乏高度连接节点的核心。我们提出一个模型来重现这些特征,证明用于提高网络安全的机制导致了所观察到的拓扑结构。出乎意料的是,我们发现由于随着时间推移针对网络拆解尝试的适应性变化,其独特结构使暗网在面对随机故障、针对性攻击和级联故障时比互联网(在描述层面用作比较基准)更具弹性。

相似文献

1
Modeling structure and resilience of the dark network.暗网的结构与弹性建模
Phys Rev E. 2017 Feb;95(2-1):022313. doi: 10.1103/PhysRevE.95.022313. Epub 2017 Feb 27.
2
Spatially embedded growing small-world networks.空间嵌入的增长小世界网络。
Sci Rep. 2014 Nov 14;4:7047. doi: 10.1038/srep07047.
3
Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain.人类大脑中基于体素的静息态功能连接的小世界和无标度组织
Neuroimage. 2008 Nov 15;43(3):528-39. doi: 10.1016/j.neuroimage.2008.08.010. Epub 2008 Aug 22.
4
Cascade-based attacks on complex networks.基于级联的复杂网络攻击。
Phys Rev E Stat Nonlin Soft Matter Phys. 2002 Dec;66(6 Pt 2):065102. doi: 10.1103/PhysRevE.66.065102. Epub 2002 Dec 20.
5
Low-rank network decomposition reveals structural characteristics of small-world networks.低秩网络分解揭示小世界网络的结构特征。
Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Dec;92(6):062822. doi: 10.1103/PhysRevE.92.062822. Epub 2015 Dec 21.
6
Functional and evolutionary inference in gene networks: does topology matter?基因网络中的功能与进化推断:拓扑结构重要吗?
Genetica. 2007 Jan;129(1):83-103. doi: 10.1007/s10709-006-0035-0. Epub 2006 Aug 8.
7
The large-scale structure of semantic networks: statistical analyses and a model of semantic growth.语义网络的大规模结构:统计分析和语义增长模型。
Cogn Sci. 2005 Jan 2;29(1):41-78. doi: 10.1207/s15516709cog2901_3.
8
Multiplex congruence network of natural numbers.自然数的多重同余网络。
Sci Rep. 2016 Mar 31;6:23714. doi: 10.1038/srep23714.
9
The responsiveness of criminal networks to intentional attacks: Disrupting darknet drug trade.犯罪网络对蓄意攻击的反应能力:破坏暗网毒品交易。
PLoS One. 2020 Sep 10;15(9):e0238019. doi: 10.1371/journal.pone.0238019. eCollection 2020.
10
Onion-like networks are both robust and resilient.洋葱状网络既坚固又有弹性。
Sci Rep. 2018 Jul 26;8(1):11241. doi: 10.1038/s41598-018-29626-w.

引用本文的文献

1
Using network science to examine audio-visual speech perception with a multi-layer graph.利用多层图的网络科学研究视听言语感知。
PLoS One. 2024 Mar 29;19(3):e0300926. doi: 10.1371/journal.pone.0300926. eCollection 2024.
2
Using Complex Networks in the Hearing Sciences.在听力科学中使用复杂网络
Ear Hear. 2024;45(1):1-9. doi: 10.1097/AUD.0000000000001395. Epub 2023 Jun 15.
3
The Resilience of the Phonological Network May Have Implications for Developmental and Acquired Disorders.语音网络的弹性可能对发育性和后天性疾病有影响。
Brain Sci. 2023 Jan 23;13(2):188. doi: 10.3390/brainsci13020188.
4
What Can Network Science Tell Us About Phonology and Language Processing?网络科学能告诉我们哪些关于音韵学和语言处理的信息?
Top Cogn Sci. 2022 Jan;14(1):127-142. doi: 10.1111/tops.12532. Epub 2021 Apr 9.
5
Distance Entropy Cartography Characterises Centrality in Complex Networks.距离熵制图法表征复杂网络中的中心性。
Entropy (Basel). 2018 Apr 11;20(4):268. doi: 10.3390/e20040268.