Manet中基于信任的Winnow线性乘性分类安全多径路由
Trust-Based Winnow Linear Multiplicative Classification For Secure Multipath Routing In Manet
摘要：
移动自组网中的多路径路由问题是保证数据安全传输的重要问题。为了克服这些限制，Winnow信任为基础的多路径路由发现（WT-MRD）机制的建议。它构造了从源到目的地的多条路径，具有更高的安全性和更短的时间。首先，通过基于邻居节点的信任计算模型（NN-TC），根据节点的合作次数、数据包转发率和丢包率计算每个节点的信任值。在计算出节点的信任值后，利用Winnow线性乘法分类算法（WLMC）将节点分为正常节点和恶意节点。在普通节点的帮助下，WT-MRD机制通过发送两个控制消息RREP和RREQ来发现从源到目的地的多径。源节点向邻居节点发送用于构建多条路由路径的路由请求Rcid。在接收到Rendering消息之后，相邻节点维护其中存在源信息和下一跳信息的路由表。然后从相邻节点向源节点发送路由应答（RREP）消息。通过这种方式，从源到目的地的多个路由路径被构造为具有更高的安全级别。保留字：合作计数，数据包丢失率，数据包转发率，多路径路由，安全性，信任值，Winnow线性乘法分类
英文摘要：
Multipath routing in Mobile Ad Hoc Network (MANET) plays a significant concern for secured data transmission by avoiding the attack nodes in the network. In order to overcome such limitations, a Winnow Trust based Multipath Route Discovery (WT-MRD) Mechanism is proposed. It constructs multiple paths from source to destination with higher security and lesser time. Initially, the trust value of each node is calculated based on the node cooperative count, data packet forwarding rate and packet drop rate by Neighbor Node-based Trust Calculation (NN-TC) Model. After calculating the trust value, the nodes are classified as normal or malicious by using Winnow Linear Multiplicative Classification (WLMC) Algorithm. With the help of normal nodes, the WT-MRD Mechanism finds multipath from source to destination by sending a two control message RREQ and RREP. Source node transmits a route request RREQ to the neighboring node for constructing the multiple route paths. After receiving the RREQ message, the neighboring node maintains the route table where the source information and next hop information are present. Then Route Reply (RREP) messages are sent from neighboring node to source node. By this way, multiple route paths from source to destination are constructed with a higher security level. Keywords: Cooperative Count, Data Packet Dropped Rate, Data Packet Forwarded Rate, Multipath Routing, Security, Trust Value, Winnow Linear Multiplicative Classification
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
