Enhancing the Robustness of Federated Learning-Based Intrusion Detection Systems in Transportation Networks

As transportation systems become more connected and automated, strong cybersecurity is essential. This paper presents a novel Intrusion Detection System (IDS) based on Federated Learning (FL) to improve security in transportation networks without compromising user privacy. FL enables multiple devices, such as vehicles and roadside units, to collaboratively train a shared model using local data. We focus on major challenges such as adversarial and data poisoning attacks that can disrupt system operations and reduce detection accuracy. To address these threats, we propose new techniques using model diversity and progressive training. These methods allow different network nodes to train slightly different models, making the system more resilient to attacks. Our experimental results on realworld datasets show that model diversity can improve accuracy by up to 25% under attack scenarios. Progressive training also helps reduce prediction errors by 40%, leading to better overall system performance. The proposed approach strengthens the robustness and adaptability of FL-based IDS in dynamic and hostile transportation environments.