基于物联网物理捕获阶段攻击检测方法研究  

Research on attack detection method in physical capture phase based on Internet of things

作　　者：高林 李捍东[1] 郑华俊 GAO Lin;LI Handong;ZHENG Huajun(School of Electrical Engineering,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学电气工程学院,贵阳550025

出　　处：《智能计算机与应用》2024年第4期151-156,共6页Intelligent Computer and Applications

基　　金：国家自然科学基金(52167007)。

摘　　要：文章以优化物联网节点攻击检测的捕获率为研究目的,以多端柔性配电网管理物联网平台中的网络节点为研究对象,针对无线传感器网络节点捕获攻击的检测问题,分析了传感器节点被捕获时的特点,建立了传感器节点分级的网络拓扑模型。在此基础上,提出了改进的物理捕获阶段攻击检测方法,除了考虑缺席时间阈值外,还引入了心跳差阈值和异常事件数量阈值等多个阈值来增强检测能力。通过OMNET++仿真平台搭建传感器网络拓扑结构并进行了仿真实验,结果表明所提出的方法在检测率等方面具有一定的优越性。This paper aims to optimize the capture rate of iot node attack detection,and takes the network nodes in the multi-terminal flexible distribution network management iot platform as the research object.Aiming at the problem of detecting node capture attacks in wireless sensor networks,this paper analyzes the characteristics of sensor nodes when they are captured,and establishes a hierarchical network topology model of sensor nodes,based on this,an improved attack detection method in physical capture phase is proposed.Besides the threshold of absence time,several thresholds such as the threshold of heartbeat difference and the threshold of the number of abnormal events are introduced to enhance the detection ability.Finally,the topology of sensor network is built by OMNET++simulation platform and the simulation results show that the proposed method has advantages in detection rate and so on.

关 键 词：心跳序列 缺席时间阈值 网络拓扑 OMNET++仿真 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]