Edge Computing-enabled Intrusion Detection for C-V2X Networks using Federated Learning

Intrusion detection systems (IDS) have already demonstrated their effectiveness in detecting various attacks in cellular vehicle-to-everything (C-V2X) networks, especially when using machine learning (ML) techniques. However, it has been shown that generating ML-based models in a centralized way consumes a massive quantity of network resources, such as CPU/memory and bandwidth, which may represent a critical issue in such networks. To avoid this problem, the new concept of Federated Learning (FL) emerged to build ML-based models in a distributed and collaborative way. In such an approach, the set of nodes, e.g., vehicles or gNodeB, collaborate to create a global ML model trained across these multiple decentralized nodes; each one with its respective data samples that are not shared with any other nodes. In this way, FL enables, on the one hand, data privacy since sharing data with a central location is not always feasible and, on the other hand, network overhead reduction. This paper designs a new IDS for C-V2X networks based on FL. It leverages edge computing to not only build a prediction model in a distributed way, but also to enable low latency intrusion detection. Moreover, we build our FL-based IDS on top of well-know CIC-IDS2018 dataset, that includes the main network attacks. Noting that, we first perform a feature engineering on the dataset using the ANOVA method to consider only the most informative features. Simulation results show the efficiency of our system compared to the existing solutions in terms of attack detection accuracy while reducing the network resource consumption.