Research on Intrusion Detection Based on CNN-BiLSTM in SDN Environment
Software Defined Network(SDN)is a new network architecture that separates the con-trol layer from the data layer.While realizing centralized management and programmability of the net-work,it also faces the problem of vulnerability to intrusion attacks.A detection and defense mechanism is designed for this problem.After using the deep learning algorithm to process the data set,the CNN-BiLSTM model is designed to detect attacks by integrating the convolutional neural network(CNN)and the bidirectional long-term and short-term memory network(BiLSTM),and the defense mechanism is designed by using SDN programmability.A network platform based on SDN is built for simulation experiments.Experimental results show that the designed method can detect intrusion traffic more accu-rately than traditional detection methods and can effectively implement defense functions after detection.
software defined networkdeep learningconvolutional neural networkbidirectional long short-term memory networkintrusion detectionance