Method of Network Intrusion Discovery Based on Convolutional Long-Short Term Memory Network and Implementation in VSS

Network intrusion discovery aims to detect the network attacks and abnormal network intrusion efficiently, that is an important protection implement in the field of cyber security. However, the traditional network intrusion discovery method are difficult to extract high-order features (such as spatial-temporal information) from network traffic data. In this paper, we proposed an improved method of network intrusion discovery based on convolutional long-short term memory network. This method implements the convolution operation in deep learning into the network structure of long-short term memory and improves the accuracy of network intrusion discovery. In the experimental section, we compared with other similar methods, the result shows that the proposed method has some advantages in the aspects of overall network intrusion discovery index, detection index of different types, and AUC evaluation index. In addition, we applied our method to the network intrusion discovery scenarios of video surveillance system (VSS). The result shows that the proposed method has advantages in accuracy, recall, precision, and other similar methods.