Federated Deep Learning for Collaborative Intrusion Detection in Heterogeneous Networks

IEEE Vehicular Technology Conference, pp. 1–6

Abstract

In this paper, we propose Federated Deep Learning (FDL) for intrusion detection in heterogeneous networks. Local Deep Neural Network (DNN) models are used to learn the hierarchical representations of the private network traffic data in multiple edge nodes. A dedicated central server receives the parameters of the local DNN models from the edge nodes, and it aggregates them to produce an FDL model using the Fed+ fusion algorithm. Simulation results show that the FDL model achieved an accuracy of 99.27 ± 0.79%, a precision of 97.03 ± 4.22%, a recall of 98.06 ± 1.72%, an F1 score of 97.50 ± 2.55%, and a False Positive Rate (FPR) of 2.40 ± 2.47%. The classification performance and the generalisation ability of the FDL model are better than those of the local DNN models. The Fed+ algorithm outperformed two state-of-the-art fusion algorithms, namely federated averaging (FedAvg) and Coordinate Median (CM). Therefore, the DNN-Fed+ model is preferable for intrusion detection in heterogeneous wireless networks.

Authors 5

1. Segun Isaiah Popoola

   Manchester Metropolitan University

   Affiliation as printed

   Manchester Metropolitan University, Manchester, UK

2. Guan Gui

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

   College of Telecommunications and Information Engineering, NJUPT, Nanjing, China

3. Bamidele Adebisi

   Manchester Metropolitan University

   Affiliation as printed

   Manchester Metropolitan University, Manchester, UK

4. Mohammad Ali A. Hammoudeh

   Manchester Metropolitan University

   Affiliation as printed

   Manchester Metropolitan University, Manchester, UK

5. Haris Gacanin Aachen Institute for Communication Technologies and Embedded Systems

   RWTH Aachen University

   Affiliation as printed

   Institute for Communication Technologies & Embedded Systems, RWTH Aachen University, Aachen, Germany