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A novel approach for the fast detection of black holes in mobile ad hoc networks

机译:一种快速检测移动自组织网络中黑洞的新颖方法

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Mobile ad hoc networks are infrastructure-less wireless networks that rely on node cooperation to properly work. In this kind of networks, attack detection and reaction is a key issue to the whole network. The most common threat in mobile ad hoc network scenarios consists in the presence of a certain percentage of selfish nodes, which try to reduce the consumption of their own resources to prolong their battery lifetime. Those nodes do not collaborate on forwarding activities, therefore affecting the overall network performance. Watchdogs are well-known mechanisms to detect threats and attacks from misbehaved and selfish nodes in computer networks. The problem behind the use of watchdogs is that while they can be quite effective in detecting selfishness by using their traffic overhearing behaviour, they can also cause a relatively high level of false negatives, thereby reducing their accuracy. This article proposes a collaborative approach for detecting selfish nodes in mobile ad hoc networks. It is based on using a set of collaborative watchdogs, which collaborate to enhance their individual and collective performance. By using both an analytical study and simulation, we demonstrate that our approach is able to improve accuracy a'hd detection speed, while reducing the impact of false-negative events.
机译:移动自组织网络是一种无基础架构的无线网络,它依赖于节点合作才能正常工作。在这种网络中,攻击检测和反应是整个网络的关键问题。移动自组织网络方案中最常见的威胁在于存在一定百分比的自私节点,这些节点试图减少自身资源的消耗以延长其电池寿命。这些节点不协作进行转发活动,因此会影响整体网络性能。看门狗是众所周知的机制,用于检测来自计算机网络中行为不当和自私的节点的威胁和攻击。使用看门狗背后的问题是,尽管它们可以通过使用其流量监听行为来非常有效地检测自私性,但它们也可能导致较高水平的假阴性,从而降低其准确性。本文提出了一种用于检测移动自组织网络中自私节点的协作方法。它基于使用一组协作监视程序,它们可以协作以增强其个人和集体绩效。通过同时使用分析研究和模拟,我们证明了我们的方法能够提高准确度和检测速度,同时减少假阴性事件的影响。

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