基于模糊数学的MANET恶意节点识别  

Malicious Node Identification in MANET Based on Fuzzy Mathematics

作　　者：王亚[1,2] 熊焰[1] 龚旭东[1] 陆琦玮[1] 

机构地区：[1]中国科学技术大学计算机科学与技术学院,合肥230027 [2]阜阳师范学院计算机与信息学院,安徽阜阳236037

出　　处：《计算机工程》2014年第5期134-138,共5页Computer Engineering

基　　金：国家自然科学基金资助项目(61170233;61232018;61300170);全国统计科研计划基金资助项目(2012LY009)

摘　　要：移动Ad hoc网络(MANET)是一种无线自组织网络,易受内部恶意节点攻击。针对由于网络内部攻击行为复杂而导致内部恶意节点不易识别的问题,提出一种基于模糊数学理论的MANET内部恶意节点识别方法。通过分析节点通信行为,建立由节点平均包转发延迟、转发率和丢包率组成的属性向量,利用最大隶属度原则进行分类识别。设置不同的仿真场景和恶意节点密度,采用NS2软件进行仿真实验,结果表明,该方法能识别多数内部恶意节点,虽然恶意节点密度对识别结果影响较大,但在恶意节点密度为30%的情况下,仍能保持96%以上的识别率和5%以下的误检率。Mobile Ad hoc Network（MANET） is a wireless Ad hoc network, and it is vulnerable to be attacked by inside malicious nodes. For the complexity of inside attack behavior, the malicious nodes are difficult to be identified. In order to solve this problem, this paper presents a method of identifying inside malicious nodes based on fuzzy mathematics. By analyzing the node’s communication behavior, it finds an attribute vector which consists of node’s average packet forwarding delay, forwarding ratio and packet loss ratio, then classifies it using the principle of maximum membership grade. Experiment simulates on the NS2 software, and sets different simulation scenarios and malicious node density. The simulation results show that the moving speed of nodes has little impact on the recognition results, while the malicious density has larger impact. Even the malicious nodes are rather dense, reaching 30%, a high recognition ratio still maintains more 96%, and the false recognition ratio is less 5%.

关 键 词：移动ADHOC网络 恶意节点 模式识别 模糊数学 隶属度 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]