A Lightweight Intrusion Risk Identification Method for IoT Based on Unbalanced Samples
Under the interference of imbalanced data,the node burden of the Internet of Things increases and is prone to light-weight intrusion.Therefore,a lightweight intrusion risk identification method for imbalanced samples in the Internet of Things is pro-posed.A lightweight intrusion risk identification model is designed based on one-dimensional Convolutional neural network.In order to make up for the lack of stability in model training,DCGAN is used to design a sample data balance algorithm to enhance the sample data.Use the enhanced sample data as the basic data for model training,and train the constructed lightweight intrusion risk identifica-tion model to achieve lightweight IoT intrusion risk identification.The test results show that compared to before pruning,the change in the effective parameter quantity of the model after pruning reaches 10 times.During training,the model quickly converges and rea-ches a training accuracy of nearly 1.Moreover,the intrusion risk identification accuracy of this method has been significantly im-proved,consistently stable at around 97％.
unbalanced samplesDCGANinternet of Thingslightweight intrusion riskone-dimensional convolutional neural network
万方数据
维普
