Abstract:  In the field of network intrusion detection， machine learning methods that manually extract features in feature engineering are generally used， but the manual feature extraction method is prone to losing important feature information； In addition， different types of attack traffic play different roles in detection， and existing algorithms generally suffer from important information loss and low accuracy in identifying attack types. A hybrid algorithm based on Convolutional Long-Short Term Memory （convLSTM） and Convolutional Neural Networks （CNN） is proposed for anomaly traffic detection in response to the aforementioned issues， Which directly use the payload of network traffic as data samples without manual extraction of complex traffic features， fully explores the structural features of traffic， extracts temporal and spatial features， and generates accurate intrusion detection feature vectors. The experimental results show that on the CIC-ISDS2017 dataset， the accuracy of the hybrid algorithm convLSTM-CNN in network intrusion detection reaches 99.39%. Compared with the simple DNN， SVM， LSTM， GRU-CNN and other models， it has a higher accuracy and lower false alarm rate， indicating that the algorithm improves the efficiency of abnormal traffic detection.

Key words:  , network security, intrusion detection, convolutional long-short term networks, convolutional neural network, deep learning