Research on intrusion detection algorithm based on CNN-LSTM model
With the wide application of cloud technology,network intrusion detection systems are be-coming more and more popular.However,general intrusion detection datasets are spatial and tem-poral,in this paper,we utilize the powerful features of Convolutional Neural Networks(CNN)as well as Long Short-Term Memory Networks(LSTM)to construct a deep learning model,CNN-LSTM,to learn the spatiotemporal properties of data.Firstly,a convolutional neural network is used to do feature selection on the traffic data,and a Dropout layer is added after the convolutional layer to prevent over fitting.Then the time series learning characteristics of the Long Short Term Memory Network are used to learn and classify the features screened by the Convolutional Neural Network in order to improve the efficiency and accuracy of the network detection.It is proved ex-perimentally that the detection results of the model used in this paper are better than single CNN and LSTM models.
Network IntrusionDeep LearningConvolutional Neural NetworkLong Short Term Mem-ory Network