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A framework for robust detection and prevention of wide-spread node compromise in wireless sensor networks.

机译:用于在无线传感器网络中可靠地检测和防止广泛的节点损害的框架。

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摘要

Wireless sensor networks are known to be vulnerable to a variety of attacks that could undermine normal sensor network operations. Many schemes have been developed to defend the wireless sensor networks against various attacks. Most of them focus on making the network and service protocols be attack-resilient rather than rooting out the source of attacks. Although the attack-resiliency approach mitigates the threats on sensor network protocols, it requires substantial time and effort for continuously enhancing the robustness of the protocols in line with the emergence of new types of attacks. Accordingly, if we are able to detect and remove the sources of attacks as soon as possible, we could save the large amount of time and effort incurred from employing the attack-resiliency approach. In wireless sensor networks, the principle sources of various attacks are compromised nodes. Specifically, since sensor nodes are deployed in an unattended manner, an adversary can physically capture and compromise sensor nodes, and mount a variety of attacks with the compromised nodes. He can also move the compromised nodes to multiple locations to evade the detection. Moreover, he can create wide-spread influence by generating many replica nodes of a few compromised nodes or propagating malicious worm into the network. Our works are designed for rooting out the sources of possible threats by quickly detecting and removing compromised nodes and preventing wide-spread node compromise through replica node and worm propagation attacks.To meet this challenge, we propose a framework for robust detection and revocation of wide-spread node compromise in wireless sensor networks. In the framework, we first propose a reputation-based trust management scheme to facilitate static node compromise detection, and then propose a distributed detection scheme to achieve fast mobile node compromise detection, and finally propose replica node detection and worm propagation detection schemes to prevent wide-spread node compromise. Specifically, the framework is composed of five components. In the first component, we quickly detect the suspected regions in which compromised nodes are likely placed and perform software attestation against the nodes in the suspected regions, leading to the detection and revocation of the compromised nodes. However, if the attacker moves the compromised nodes to multiple locations in the network, such as by employing simple robotic platforms or moving the nodes by hand, he can evade the detection scheme in the first component. To resolve this limitation, we propose the second component in which we quickly detect these mobile malicious nodes that are silent for unusually many time periods---such nodes are likely to be moving---and block them from communicating in fully distributed manner.To reduce the time and effort incurred from directly compromising many benign nodes, attacker may launch replica node attacks in which he generates many replica nodes of a few compromised nodes and widely spread them over the network. To thwart wide-spread node compromise by replica node attacks, we propose two complementary schemes for replica detection as the third and fourth components. In the third component, we detect static replica nodes by leveraging the intuition that static replica nodes are placed in more than one location. In the fourth component, we quickly detect mobile replicas by leveraging the intuition that mobile replicas are in two or more locations at once and thus appear to move much faster than benign nodes, leading to highly likely exceed the predefined maximum speed.However, the attacker needs to prepare as many sensor nodes as the number of replicas that he wants to generate in replica node attacks. Thus, the attack costs will increase in proportion to the number of deployed replicas. To reduce these costs, the attacker may attempt to widely spread node compromise by capturing a few nodes and having the captured nodes propagate malicious worm through the network, leading to the fast compromise of many benign nodes. To fight against this type of attack, we propose the fifth component in which we quickly detect worm propagation in fully distributed fashion by leveraging the intuition that a worm's communication pattern is different from benign traffic.Through analysis and experimental study, we show that these components achieve robust and effective detection and revocation capability of node compromise, replica node, worm propagation with reasonable overhead.
机译:众所周知,无线传感器网络容易受到各种攻击的破坏,这些攻击可能破坏传感器网络的正常运行。已经开发出许多方案来保护无线传感器网络免受各种攻击。它们中的大多数都集中在使网络和服务协议具有抗攻击性,而不是根除攻击源。尽管防攻击方法减轻了传感器网络协议的威胁,但它需要大量的时间和精力来不断增强协议的鲁棒性,以适应新型攻击的出现。因此,如果我们能够尽快检测到并消除攻击源,则可以节省采用攻击防御方法带来的大量时间和精力。在无线传感器网络中,各种攻击的主要来源都是受感染的节点。具体来说,由于传感器节点是以无人值守的方式部署的,因此攻击者可以物理捕获并破坏传感器节点,并使用受到感染的节点进行各种攻击。他还可以将受到感染的节点移动到多个位置,以逃避检测。此外,他可以通过生成几个受损节点的许多副本节点或将恶意蠕虫传播到网络中来产生广泛的影响。我们的工作旨在通过快速检测和删除受感染的节点并通过复制节点和蠕虫传播攻击来防止广泛的节点泄漏来根除可能的威胁源。为解决这一挑战,我们提出了一个强大的检测和撤消广域网框架无线传感器网络中的节点扩散问题。在该框架中,我们首先提出一种基于信誉的信任管理方案,以方便进行静态节点入侵检测,然后提出一种分布式检测方案,以实现快速的移动节点入侵检测,最后提出副本节点检测和蠕虫传播检测方案,以防止广泛使用。 -扩散节点危害。具体来说,该框架由五个组件组成。在第一个组件中,我们快速检测到可能存在受感染节点的可疑区域,并针对可疑区域中的节点执行软件认证,从而导致对受感染节点的检测和吊销。但是,如果攻击者将受感染的节点移动到网络中的多个位置(例如通过采用简单的机器人平台或用手移动节点),则可以逃避第一组件中的检测方案。为了解决此限制,我们提出了第二个组件,在该组件中,我们可以快速检测这些在异常多个时间段内处于静默状态的移动恶意节点-这些节点可能正在移动-并阻止它们以完全分布式的方式进行通信。为了减少直接损害许多良性节点所花费的时间和精力,攻击者可能会发起副本节点攻击,在攻击中,他会生成几个受感染节点的很多副本节点,并将它们广泛传播到网络上。为了阻止复制节点攻击来破坏广泛的节点,我们提出了两个互补的方案作为第三和第四部分来进行复制检测。在第三个组件中,我们通过利用将静态副本节点放置在多个位置的直觉来检测静态副本节点。在第四个组件中,我们利用直觉一次发现移动副本位于两个或多个位置,因此其移动速度似乎快于良性节点,从而迅速检测到移动副本,从而很可能超过了预定的最大速度。需要准备与他要在副本节点攻击中生成的副本数量一样多的传感器节点。因此,攻击成本将与已部署副本的数量成比例地增加。为了降低这些成本,攻击者可能试图通过捕获几个节点并使捕获的节点通过网络传播恶意蠕虫,从而广泛传播节点危害,从而导致许多良性节点的快速危害。为了对抗这种类型的攻击,我们提出了第五个组件,其中我们利用蠕虫的通信模式不同于良性流量的直觉来快速以完全分布式的方式检测蠕虫的传播。通过分析和实验研究,我们证明了这些组件以合理的开销实现健壮和有效的节点破坏,副本节点,蠕虫传播的检测和撤消功能。

著录项

  • 作者

    Ho, Jun-Won.;

  • 作者单位

    The University of Texas at Arlington.;

  • 授予单位 The University of Texas at Arlington.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 183 p.
  • 总页数 183
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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