A Federated Deep Transfer Learning Algorithm for Intrusion Detection

With the increasing expansion and complexity of cyberspace data and traffic, network security threats have also increased sharply. As one of the important means to ensure the security of information systems, intrusion detection is facing unprecedented challenges. In this paper, we propose a federated deep transfer learning algorithm, transfer-enhanced deep and transfer domain adaptation (TEDTDA), for intrusion detection. TEDTDA uses federated learning to train local models using intrusion detection data from multiple organizations to protect data privacy. It improves the efficiency of model training by integrating transfer learning theory and knowledge transfers. Moreover, it eliminates unreliable and low-quality local models through model selection in the training process to improve the detection effect. The algorithm is tested on three intrusion detection datasets: ISCX2012, NSL-KDD, and CICIDS2017. Compared with the benchmark algorithm, the proposed TEDTDA algorithm significantly improves the detection accuracy, training efficiency, and other key performance indicators.