Monitoring Algorithm in Malicious Vehicular Adhoc Networks

S. Padmapriya, R. Valli, M. Jayekumar
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引用次数: 2

Abstract

Vehicular Adhoc Networks (VANETs) ensures road safety by communicating with a set of smart vehicles. VANET is a subset of Mobile Adhoc Networks (MANETs). VANET enabled vehicles helps in establishing communication services among one another or with the Road Side Unit (RSU). Information transmitted in VANET is distributed in an open access environment and hence security is one of the most critical issues related to VANET. Although each vehicle is not a source of all communications, most contact depends on the information that other vehicles receive from it. That vehicle must be able to assess, determine and respond locally on the information obtained from other vehicles to protect VANET from malicious act. Of this reason, message verification in VANET is more difficult due to the protection and privacy issues of the participating vehicles. To overcome security threats, we propose Monitoring Algorithm that detects malicious nodes based on the pre-selected threshold value. The threshold value is compared with the distrust value which is inherently tagged with each vehicle. The proposed Monitoring Algorithm not only detects malicious vehicles, but also isolates the malicious vehicles from the network. The proposed technique is simulated using Network Simulator2 (NS2) tool. The simulation result illustrated that the proposed Monitoring Algorithm outperforms the existing algorithms in terms of malicious node detection, network delay, packet delivery ratio and throughput, thereby uplifting the overall performance of the network.
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恶意车载Adhoc网络中的监控算法
车辆自组织网络(VANETs)通过与一组智能车辆通信来确保道路安全。VANET是移动自组网(manet)的一个子集。启用VANET的车辆有助于在彼此之间或与路侧单元(RSU)建立通信服务。在VANET中传输的信息分布在一个开放的访问环境中,因此安全是与VANET相关的最关键问题之一。虽然每辆车不是所有通信的来源,但大多数联系依赖于其他车辆从它那里接收到的信息。该车辆必须能够评估、确定并在当地对从其他车辆获得的信息作出反应,以保护VANET免受恶意行为的侵害。因此,由于参与车辆的保护和隐私问题,VANET中的消息验证更加困难。为了克服安全威胁,我们提出了基于预先选择的阈值检测恶意节点的监控算法。将阈值与每个车辆固有标记的不信任值进行比较。所提出的监控算法不仅能检测出恶意车辆,还能将恶意车辆从网络中隔离出来。采用Network Simulator2 (NS2)工具对该技术进行了仿真。仿真结果表明,本文提出的监控算法在恶意节点检测、网络时延、数据包投递率和吞吐量等方面都优于现有算法,从而提高了网络的整体性能。
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