Malicious node detection in OppNets using hash chain technique
Malicious node detection in OppNets using hash chain technique
复制标题
使用哈希链技术检测 OppNets 中的恶意节点
DOI：
10.1109/iccsnt.2015.7490890
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
R. Doss
中科院分区：
文献类型：
--
作者：
Majeed Alajeely;Asma'a Ahmad;R. Doss
自组织网络的目标是建立一个可靠的网络，其中节点没有端到端的连接，通信链路经常遭受频繁中断和长延迟。OppNets路由协议的设计面临着数据机密性和完整性保护等严峻挑战。OppNets利用人类社会的特征，如相似性，日常生活，移动模式和兴趣来执行消息路由和数据共享。数据包丢弃攻击是入侵网络中最难的攻击之一，因为源节点和目的节点都不知道数据包将在何时何地被丢弃。在本文中，我们提出了一种新的恶意节点检测技术，对数据包伪造攻击的恶意节点丢弃一个或多个数据包，而不是他们注入新的假数据包。在我们以前的作品中，我们称这种新颖的攻击为数据包伪造攻击。入侵网络中的每个节点都可以检测并追踪恶意节点，这是基于一个坚实而强大的思想，即哈希链技术。在我们基于哈希链的防御技术中，我们有两个阶段。第一阶段是检测攻击，第二阶段是发现恶意节点。我们比较了我们的方法与确认为基础的机制和网络编码为基础的机制，这是众所周知的方法在文献中。在我们的仿真中，我们已经取得了非常高的节点检测精度和低的误报率。
Opportunistic Networks aim to set a reliable networks where the nodes has no end-to-end connection and the communication links often suffer from frequent disruption and long delays. The design of the OppNets routing protocols is facing a serious challenges such as the protection of the data confidentiality and integrity. OppNets exploit the characteristics of the human social, such as similarities, daily routines, mobility patterns and interests to perform the message routing and data sharing. Packet dropping attack is one of the hardest attacks in Opportunistic Networks as both the source nodes and the destination nodes have no knowledge of where or when the packet will be dropped. In this paper, we present a new malicious nodes detection technique against packet faking attack where the malicious node drops one or more packets and instead of them injects new fake packets. We have called this novel attack in our previous works a packet faking attack. Each node in Opportunistic Networks can detect and then traceback the malicious nodes based on a solid and powerful idea that is, hash chain techniques. In our hash chain based defense techniques we have two phases. The first phases is to detect the attack, and the second phases is to find the malicious nodes. We have compared our approach with the acknowledgement based mechanisms and the networks coding based mechanism which are well known approaches in the literature. In our simulation, we have achieved a very high node detection accuracy and low false negative rate.