Towards Enhancing Privacy-Preservation of a Federated Learning CNN Intrusion Detection System in IoT: Method and Empirical Study

Abstract

Enormous risks and hidden dangers of information security exist in the applications of Internet of Things (IoT) technologies. To secure IoT software systems, software engineers have to deploy advanced security software such as Intrusion Detection Systems (IDS) that are able to keep track of how the IoT devices behave within the network and detect any malicious activity that may be occurring. Considering that IoT devices generate large amounts of data, Artificial intelligence (AI) is often regarded as the best method for implementing IDS thanks to AI's high capability in processing large amounts of IoT data. To tackle these security concerns, specifically the ones tied to the privacy of data used in IoT systems, the software implementation of a Federated Learning (FL) method is often used to improve both privacy preservation (PP) and scalability in IoT networks. In this paper, we present a FL IDS that leverages a 1 Dimensional Convolutional Neural Network (CNN) for efficient and accurate intrusion detection in IoT networks. To address the critical issue of PP in FL, we incorporate three techniques: Differential Privacy, Diffie–Hellman Key Exchange, and Homomorphic Encryption. To evaluate the effectiveness of our solution, we conduct experiments on seven publicly available IoT datasets: TON IoT, IoT-23, Bot-IoT, CIC IoT 2023, CIC IoMT 2024, RT-IoT 2022, and EdgeIIoT. Our CNN-based approach achieves outstanding performance with an average accuracy, precision, recall, and F1-score of 97.31%, 95.59%, 92.43%, and 92.69%, respectively, across these datasets. These results demonstrate the effectiveness of our approach in accurately identifying and detecting intrusions in IoT networks. Furthermore, our experiments reveal that implementing all three PP techniques only incurs a minimal increase in computation time, with a 10% overhead compared to our solution without any PP mechanisms. This finding highlights the feasibility and efficiency of our solution in maintaining privacy while achieving high performance. Finally, we show the effectiveness of our solution through a comparison study with other recent IDS trained and tested on the same datasets we use.