The Internet and computer networks are currently suffering from serious security threats. Those threats often keep changing and will evolve to new unknown variants. In order to maintain the security of network, we design and implement a dynamic network anomaly detection system using deep learning methods. We use Long Short Term Memory (LSTM) to build a deep neural network model and add an Attention Mechanism (AM) to enhance the performance of the model. The SMOTE algorithm and an improved loss function are used to handle the class-imbalance problem in the CSE-CICIDS2018 dataset. The experimental results show that the classification accuracy of our model reaches 96.2%, which is higher than other machine learning algorithms. In addition, the class-imbalance problem is alleviated to a certain extent, making our method have great practicality