{"title":"Adopting cross layer approach for detecting and segregating malicious nodes in MANET","authors":"J. Vinayagam, C. Balaswamy, K. Soundararajan","doi":"10.1109/CSPC.2017.8305890","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a cross layer approach to detect the malicious node in MANET. We develop a cross layer data monitoring algorithm in order to correlate the MAC (Medium Access Control) layer parameters with network layer parameters for effectively detecting malicious nodes from the network. For segregating the malicious nodes, our approach utilizes both the single and cross layer parameters. information about detected malicious node ids from the network is broadcasted to the other nodes in the network. The routing protocol which is used in the simulation is AODV (Ad Hoc On-Demand Distance Vector). The performance of our technique is proved by the simulation of our system model using the network simulator NS-2. Experimental analysis reveals that our proposed approach defends the Black hole attack with better performance in terms of packet loss ratio, packet delivery ratio and Normalized Routing Load.","PeriodicalId":123773,"journal":{"name":"2017 International Conference on Signal Processing and Communication (ICSPC)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Signal Processing and Communication (ICSPC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSPC.2017.8305890","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, we propose a cross layer approach to detect the malicious node in MANET. We develop a cross layer data monitoring algorithm in order to correlate the MAC (Medium Access Control) layer parameters with network layer parameters for effectively detecting malicious nodes from the network. For segregating the malicious nodes, our approach utilizes both the single and cross layer parameters. information about detected malicious node ids from the network is broadcasted to the other nodes in the network. The routing protocol which is used in the simulation is AODV (Ad Hoc On-Demand Distance Vector). The performance of our technique is proved by the simulation of our system model using the network simulator NS-2. Experimental analysis reveals that our proposed approach defends the Black hole attack with better performance in terms of packet loss ratio, packet delivery ratio and Normalized Routing Load.
在本文中,我们提出了一种跨层的方法来检测MANET中的恶意节点。为了有效地检测网络中的恶意节点,我们开发了一种跨层数据监控算法,将MAC层参数与网络层参数相关联。为了分离恶意节点,我们的方法同时利用了单层和跨层参数。从网络中检测到的恶意节点id信息被广播到网络中的其他节点。仿真中使用的路由协议是AODV (Ad Hoc On-Demand Distance Vector)。利用网络模拟器NS-2对系统模型进行仿真,验证了该技术的有效性。实验分析表明,该方法在丢包率、包投递率和归一化路由负载方面具有更好的防御黑洞攻击的性能。