https://doi.org/10.1051/epjconf/202636704007
Secure FedIDS - Privacy preserving IDS with ensemble deep learning approach
School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Chennai, India
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Published online: 29 April 2026
Abstract
People are increasingly reliant on the internet for communication and various devices in their daily lives. The Internet of Things (IoT) has dramatically changed many industries by allowing more automation and easy data sharing, but it also has a high security risk because it exposes many entry points for hackers. An Intrusion Detection System (IDS) is therefore necessary to signal the occurrence of threats in this type of environment. With the advancements in machine learning and deep learning frameworks, these areas have attracted considerable attention in the field of network security. The current study introduces SecureFedIDS, a new approach to network security which utilizes a hybrid ensemble of CNN and LSTM. To solve the data privacy problem, SecureFedIDS implements Federated Learning through the Flower framework. Experimental results reveal that the methods are highly effective in terms of detection rates and precision, reaching 99.4% for binary classification and 97% for multiclass classification with a minimal number of false alarms.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
