Support vector machine through detecting packet dropping misbehaving nodes in MANET

Ravi Parihar, Ashish Jain, Upendra Singh
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引用次数: 9

Abstract

Mobile ad-hoc network (MANET) is suffering from various attacks due to its infrastructure-less characteristics. Hence, MANET needs a variety of specific security methods to detect the false entrance of the misbehaving nodes. The network works well if the nodes are trusty and act rightly and cooperatively. In this paper, we are identifying and detecting the packet dropping nodes using the Support vector machine (SVM). A SVM is reactively used to classify the nodes in two different classes, either the normal or malicious nodes. SVM takes as an input the neighbor trust value, calculated with the data packets and control packets. Our technique is implemented with Ad-hoc on demand vector routing protocol (AODV). Our experimental results evaluated using the packet delivery ratio (PDR), End-To-End delay, Average throughput.
支持向量机通过检测MANET中丢包行为不端的节点
移动自组织网络(MANET)由于其无基础设施的特点,正遭受各种攻击。因此,MANET需要各种特定的安全方法来检测行为不端的节点的假入口。如果节点是可信的,并且行为正确和合作,网络就会运行良好。在本文中,我们使用支持向量机(SVM)来识别和检测丢包节点。支持向量机被被动地用于将节点分为两类,正常或恶意节点。SVM以邻居信任值作为输入,由数据包和控制数据包计算得到。我们的技术是用Ad-hoc按需向量路由协议(AODV)实现的。我们的实验结果使用分组传输比(PDR),端到端延迟,平均吞吐量进行评估。
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