Existing intrusion detection models trained by machine learning all need reliable datasets. However, the update of the public dataset is basically long after the occurrence of the new attack, which makes the update speed of the intrusion detection model relatively slow. In this paper,we proposed a Never-Ending learning framework for intrusion detection. In this framework, the neural network model can constantly absorb the knowledge of the public/private datasets using multi-task learning and transfer learning. Meanwhile, the framework also drew on the idea of serendipitous learning, updating the model by isolating the suspected traffic from the device under attack and classifying it as a new attack category. In order to protect the privacy of users and private datasets, this paper improves various training methods of continuous learning based on the idea of federated learning. As a result, users’ data will not be transmitted directly, so as to protect users’ privacy.