【24h】

Dangerous Graphs

机译:危险图

获取原文

摘要

Anomalies and faults are inevitable in computer networks, today more than ever before. This is due to the large scale and dynamic nature of the networks used to process big data and to the ever-increasing number of ad-hoc devices. Beyond natural faults and anomalies occurring in a network, threats proceeding from attacks conducted by malicious intruders must be considered. Consequently, there is often a need to quickly isolate and even repair a fault in a network when it appears. Furthermore, despite the presence in a network of faults stemming from malicious entities, we need to identify the latter and their behaviours, and develop protocols resilient to their attacks. Thus, defining models to capture the dangers inherent to various faults, anomalies and threats in a network and studying such threats, has become increasingly important and popular. Threats in networks can be of two kinds: either mobile or stationary. A malicious mobile process can move along the network, whereas a stationary harmful process resides in a host. One of the most studied models for stationary harmful processes is the black hole, which was introduced by Dobrev, Flocchini, Prencipe and Santoro in 2001. A black hole models a network node in which a destructive process deletes any visiting agent or incoming data upon arrival, without leaving any observable trace. Conversely, a network may face one or more malicious mobile processes infecting one or more nodes. Given both kinds of threats, a first crucial task consists in searching for and reporting as quickly as possible the location all faulty nodes while using a minimum number of mobile agents. In general, the main issue is to identify the minimal hypotheses under which faulty nodes can be found. This problem has been investigated in both asynchronous and synchronous networks. A corollary task is to make sure that the protocols designed for solving problems such as gathering and transferring data still work despite the presence of one or more faulty nodes. In this chapter, we review the state-of-the-art of research pertaining to the presence of faulty nodes in a network. We discuss different models in synchronous and asynchronous networks and for different communication and computation capabilities of the agents. We also address relevant computational issues and present algorithmic techniques and impossibility results.
机译:在计算机网络中,异常和故障是不可避免的,如今比以往任何时候都多。这是由于用于处理大数据的网络的大规模和动态性质以及自组织设备的数量不断增加。除了网络中发生的自然故障和异常外,还必须考虑恶意入侵者发起的攻击所带来的威胁。因此,经常需要迅速隔离甚至修复网络中出现的故障。此外,尽管在网络中存在源于恶意实体的故障,我们仍需识别恶意实体及其行为,并开发可抵御其攻击的协议。因此,定义模型以捕获网络中各种故障,异常和威胁所固有的危险并研究此类威胁已变得越来越重要和流行。网络中的威胁可以分为两种:移动威胁或固定威胁。恶意的移动进程可以沿着网络移动,而静态的有害进程驻留在主机中。黑洞是研究最多的静态有害过程模型之一,该黑洞由Dobrev,Flocchini,Prencipe和Santoro于2001年引入。黑洞为网络节点建模,该网络节点中的破坏性过程会在到达时删除任何来访代理或传入数据,而不会留下任何可见的痕迹。相反,网络可能面临感染一个或多个节点的一个或多个恶意移动进程。对于这两种威胁,首要任务是在使用最少数量的移动代理的同时,尽快搜索并报告所有故障节点的位置。通常,主要问题是确定可在其中找到故障节点的最小假设。已经在异步和同步网络中研究了此问题。一个必然的任务是确保尽管存在一个或多个故障节点,但为解决诸如收集和传输数据之类的问题而设计的协议仍然可以正常工作。在本章中,我们回顾了与网络中故障节点的存在有关的最新研究。我们讨论了同步和异步网络中的不同模型,以及代理的不同通信和计算能力。我们还将解决相关的计算问题,并介绍算法技术和不可能的结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号