基于SVM的VANET黑洞攻击检测方法  

Black hole attack detection method of VANET based on SVM

作　　者：叶雪梅[1] 许馨月[1] 朱云杰[1] 戴宇佳 蔡艳宁[1] 范青刚[1] 

机构地区：[1]西安高技术研究所计算机教研室,陕西西安710025 [2]西安高技术研究所202教研室,陕西西安710025

出　　处：《传感器与微系统》2015年第6期58-60,63,共4页Transducer and Microsystem Technologies

基　　金：军队内部科研资助项目(EP133070)

摘　　要：车载自组网(VANET)在遭受黑洞攻击时会丢失数据分组,影响网络正常通信。提出了针对黑洞攻击的特征向量选择方法,建立了正常情况和黑洞攻击情况下的车队仿真模型,并采用支持向量机(SVM)的方法对VANET进行黑洞入侵检测。仿真结果表明:对于非源、目的节点以及攻击节点,采用该检测方法时都能够保持较高的检测率和较低的误检率,达到了基于单个节点通信数据判断当前VANET是否遭受黑洞攻击的较好效果。Vehicular Ad Hoc network（ VANET）will lose data packet when suffered from the black hole attack, which may affect the normal network communications. Put forward a selection method of feature vector,aiming at the black hole attack,and establish a fleet simulation model under the normal condition and the black hole attack, and use the method of support vector machine（ SVM ） to detect the intrusion of black hole of the VANET. Simulation results show that for the nodes which are not source nodes,destination nodes and attacking nodes,using this detection method can maintain high detection rate and low false detecting rate,and can achieve a good effect in judging whether the VANET suffered from the black hole attack based on communication data from single node.

关 键 词：车载自组网 黑洞攻击 支持向量机 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]