Intelligent intrusion detection based on federated learning aided long short-term memory

Deep learning based intelligent intrusion detection (IID) methods have been received strongly attention for computer security protection in cybersecurity. All these learning models are trained at either a single user server or centralized server. For one thing, it is almost impossible to train a powerful deep learning model at a single user. For other, it will encounter intrusion risks at centre server and violate user privacy if collecting dataset from all of user servers. In order to solve these problems, this paper proposes an effective IID method based on federated learning (FL) aided long short-term memory (FL-LSTM) framework. First, the initial LSTM global model is deployed at all of user servers. Second, each user trains its single model and then uploads its model parameters to central server. Finally, the central server performs model parameters aggregation to form a new global model and distributes it to user servers. Use this step as a loop for communication to complete the training of the intrusion detection model. Simulation results show that our proposed method achieves a higher accuracy and better consistency than conventional methods. (C) 2020 Elsevier B.V. All rights reserved.