Simulation of Vulnerability Detection of Sensor Network Core Nodes Based on Machine Learning
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 ma-chine 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.
Support vector machine tree multi-classifierFeature extractionPrincipal component analysisLinear Hash functionEuclidean distance