基于机器学习的传感网核心节点漏洞检测仿真  

Simulation of Vulnerability Detection of Sensor Network Core Nodes Based on Machine Learning

作　　者：徐寅森 李红艳 张子栋[2] XU Yin-sen;LI Hong-yan;ZHANG Zi-dong(Department of Computer Engineering,Shangqiu University,Shangqiu Henan 476000,China;College of Computer Engineering,Jimei University,Xiamen Fujian 361021,China)

机构地区：[1]商丘学院计算机工程学院,河南商丘476000 [2]集美大学计算机工程学院,福建厦门361021

出　　处：《计算机仿真》2024年第3期410-414,共5页Computer Simulation

基　　金：河南省高等学校精品在线开放课程(教高[2019]671)。

摘　　要：传感网的核心节点具有能量受限、难补给的特点,导致节点轮休时易出现的覆盖漏洞问题,造成传感网监测盲区。为此提出基于机器学习的传感网核心节点漏洞检测方法。利用支持向量机树形多分类器获取核心节点的位置。采取主成分分析法提取核心节点特征,将其输入到LSTM长短记忆神经网络模型中,并利用滑动窗口与哈希函数训练漏洞检测分类模型,完成传感网核心节点的漏洞检测。实验结果表明,研究方法检测传感网漏洞时平均耗时为13.6ms,检测率和准确率均可高达95%,计算得到性能消耗低于10%,90%的用户响应时间均在50ms以内。At present,the core node in the sensor network has the characteristics of limited energy and difficult supply,leading to the blind area of sensor network monitoring.Therefore,a method of detecting the vulnerability of core nodes in the sensor network was proposed based on machine learning.At first,the tree-based support vector machine multi-classifier was used to obtain the location of the core node.Then,the principal component analysis method was used to extract the characteristics of core nodes and input them into the LSTM long-short memory neural network model.Meanwhile,the sliding window and hash function were used to train the vulnerability detection classification model.Finally,the vulnerability detection of core nodes in the sensor network was completed.Experimental results prove that the average time of detecting sensor network vulnerabilities is 13.6ms.The detection rate and accuracy can reach 95%,and the performance cost is less than 10%.In addition,the response time of 90%of users is within 50ms.

关 键 词：支持向量机树型多分类器 特征提取 主成分分析 线性哈希函数 欧氏距离 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置]