Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT
This addresses security and privacy concerns in connected and autonomous vehicles by enabling efficient intrusion detection on resource-constrained edge devices, though it appears incremental as it builds on existing federated learning methods.
The paper tackles the challenge of deploying federated learning-based intrusion detection systems in transportation IoT networks with limited edge device resources by proposing a hybrid server-edge framework that offloads pre-training to a central server and enables lightweight fine-tuning on edge devices, resulting in up to 42% reduced memory usage, 75% faster training times, and up to 99.2% accuracy.
The rapid advancement of machine learning (ML) and on-device computing has revolutionized various industries, including transportation, through the development of Connected and Autonomous Vehicles (CAVs) and Intelligent Transportation Systems (ITS). These technologies improve traffic management and vehicle safety, but also introduce significant security and privacy concerns, such as cyberattacks and data breaches. Traditional Intrusion Detection Systems (IDS) are increasingly inadequate in detecting modern threats, leading to the adoption of ML-based IDS solutions. Federated Learning (FL) has emerged as a promising method for enabling the decentralized training of IDS models on distributed edge devices without sharing sensitive data. However, deploying FL-based IDS in CAV networks poses unique challenges, including limited computational and memory resources on edge devices, competing demands from critical applications such as navigation and safety systems, and the need to scale across diverse hardware and connectivity conditions. To address these issues, we propose a hybrid server-edge FL framework that offloads pre-training to a central server while enabling lightweight fine-tuning on edge devices. This approach reduces memory usage by up to 42%, decreases training times by up to 75%, and achieves competitive IDS accuracy of up to 99.2%. Scalability analyses further demonstrates minimal performance degradation as the number of clients increase, highlighting the framework's feasibility for CAV networks and other IoT applications.