基于深度卷积神经网络的物联网异构信息安全传输算法
The heterogeneous information security transmission algorithm of loT based on deep convolutional neural network
王庆宇 1余战秋1
作者信息
- 1. 安徽工业经济职业技术学院计算机与艺术学院,安徽合肥 230051
- 折叠
摘要
为了提高物联网信息传输的安全性,提出基于深度卷积神经网络的物联网异构信息安全传输算法.在建立卷积神经网络基础架构的基础上构建深度卷积神经网络模型,利用均值池化方法计算异构数据特征点的平均值,分类异构数据特征,完成物联网异构数据特征识别.对特征识别后的物联网异构数据进行非对称加密,结合数字签名技术完成物联网异构数据安全传输.仿真测试结果表明,方法的时间复杂度、响应时间、丢包率均较低,且带宽利用率较高.
Abstract
In order to improve the security of information transmission in the Internet of Things,a secure transmission algorithm of heterogeneous information in the Internet of Things based on deep convolutional neural network is proposed.Based on the establishment of convolutional neural network infrastructure,a deep convolutional neural network model is constructed,and the average value of heterogeneous data feature points is calculated by means of mean pooling method to classify heterogeneous data features and complete the feature recognition of heterogeneous data in the Internet of Things.Asymmetric encryption is performed on heterogeneous data of the Internet of Things after feature recognition,and the secure transmission of heterogeneous data of the Internet of Things is completed by combining digital signature technology.The simulation test results show that the time complexity,response time and packet loss rate of this method are low,and the bandwidth utilization rate is high.
关键词
深度卷积神经网络/物联网/异构信息/安全传输Key words
deep convolutional neural network/Internet of Things/heterogeneous information/secure transmission引用本文复制引用
出版年
2024
NETL
NSTL
万方数据