Empowering Urban Connectivity in Smart Cities using Federated Intrusion Detection

The advent of transformative technologies such as the Internet of Things (IoT) has brought forth significant advancements in various sectors like smart cities, fintech, learning, and healthcare, as well as revolutionized online activities. The IoT has facilitated widespread connectivity by interconnecting numerous objects and services, but it has also made IoT and cloud infrastructures susceptible to cyberattacks, making cybersecurity a paramount concern, particularly for the development of reliable IoT systems, especially those powering smart city networks. In this research endeavor, we embark on exploring a cutting-edge pipeline that amalgamates federated deep learning with a trusted authority approach to tackle the intricate challenges associated with intrusion detection in smart city networks. To identify anomalies and intrusions effectively within the network, we devise an improved LSTM (Long Short-Term Memory) model. Additionally, we propose an intelligent swarm optimization solution to address dimensionality reduction concerns. Thorough evaluations of our federated learning-based approach are conducted, and these are juxtaposed with several basic approaches, utilizing the renowned NSL-KDD dataset. Encouragingly, our findings reveal that the proposed framework remarkably outperforms the baseline solutions, particularly when dealing with datasets containing a substantial volume of transactions. Furthermore, our method ensures robust data security for the model, as it becomes the pioneering endeavor to incorporate the principle of trusted authority into the realm of federated learning for the management of smart city networks.