A CNN+LSTM Based Deep Learning Approach with Lagrange Interpolation for Network Intrusion Detection

With the development of 5G technology, internet traffic has surged dramatically, presenting severe challenges to cybersecurity. Considering that network traffic exhibits temporal correlation, it can be used as a basis for identifying network attacks. In this paper, a joint framework of network traffic interpolation and intrusion detection is proposed. First, by leveraging historical network traffic, traffic characteristics can be obtained, allowing for preemptive prevention of network attacks. To obtain network features at equal time intervals, the Lagrange interpolation method is introduced. Second, a deep learning algorithm combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) Networks is proposed to extract spatiotemporal features from network traffic, distinguishing between normal and abnormal network activities. Experimental results based on real network traffic dataset show that the intrusion detection accuracy using CNN and LSTM can reach above 90%, which shows that the proposal can achieve reliable intrusion detection to defend against cyber attacks.