计算机与现代化 ›› 2020, Vol. 0 ›› Issue (07): 38-42.doi: 10.3969/j.issn.1006-2475.2020.07.008

• 信息安全 • 上一篇    下一篇

基于节点蓄意攻击的因特网鲁棒性研究

  

  1. (南华大学计算机学院,湖南衡阳421001)
  • 出版日期:2020-07-06 发布日期:2020-07-15
  • 作者简介:杨泉(1994-),男,湖南邵阳人,硕士研究生,研究方向:网络安全,复杂网络,E-mail: yangquan19941024@163.com; 丁琳(1981-),女,副教授,博士,研究方向:网络科学,复杂网络,E-mail: linding@usc.edu.cn。
  • 基金资助:
    国家自然科学基金资助项目(61403183)

Research on Internet Robustness Based on Node Intentional Attacks

  1. (School of Computer, University of South China, Hengyang 421001, China)

  • Online:2020-07-06 Published:2020-07-15

摘要: 现实网络遭受蓄意攻击的鲁棒性一直是网络科学研究中的重要问题。本文通过考虑实际的因特网,采用节点度的幂函数来定义节点的初始负载,构建局域负载重分配下的级联模型,比较2种不同攻击策略对网络鲁棒性的影响,并研究蓄意攻击条件下重要网络参数对网络鲁棒性的影响。通过数值仿真实验,得出以下结论:1)当初始负载参数大于某一个阈值时,攻击高负载节点的确比攻击低负载节点对网络的危害更大,但当初始负载参数小于该阈值时,攻击低负载节点反而能更有效地破坏网络;2)节点的初始负载参数越小,容量参数越大,网络的鲁棒性越强。本文研究结果可为因特网中级联故障的控制和防御提供参考。

关键词: 级联故障, 因特网, 阈值, 蓄意攻击

Abstract: The robustness of real networks to deliberate attacks has always been an important issue in network science. By considering the actual Internet, the power function of node degree is used to define the initial load of nodes, and the cascading model under local load redistribution is constructed. The effects of two different attack strategies on network robustness are compared, and the influence of important network parameters on network robustness under intentional attack is studied. Through the numerical simulation experiment, the following conclusions are drawn: 1) When the initial load parameter is greater than a certain threshold, attacking high load nodes does harm to the network more than attacking low load nodes, but when the initial load parameter is less than the threshold, attacking low load nodes can destroy the network more effectively; 2) The smaller the initial load parameter, the larger the capacity parameter of a node, the stronger the robustness of the network. The research results of this paper can provide reference for the control and defense of cascading failures on the Internet.

Key words: cascading failures, Internet, threshold, intentional attacks

中图分类号: