FL-IDS: Federated Learning-Based Intrusion Detection System Using Edge Devices for Transportation IoT

Abstract

A federated learning-based intrusion detection system (FL-IDS) is introduced to enhance the security of vehicular networks in the context of IoT edge device implementations. The FL-IDS system protects data privacy by using local learning, in which devices share only model updates with an aggregation server. The server then generates an enhanced detection model. The FL-IDS system also incorporates a detection model (LR-IDS, PCC-CNN) based on machine learning (ML) and deep learning (DL) classifiers, namely logistic regression (LR) and convolution neural networks (CNN), to prevent attacks in transportation IoT environments. The proposed FL-IDS model uses embedded devices (such as Raspberry Pi for the client and Jetson Xavier for the server model). The real-time performance of the proposed IDS was evaluated using two different datasets, NSL-KDD and Car-Hacking. We deployed our IDS model on different architectures, testbed 1 (with 2 clients) and testbed 2 (with 4 clients). The model evaluation has been evaluated based on the accuracy, and loss parameters. The results show that the FL-IDS system outperforms traditional centralized learning with machine learning and deep learning approaches regarding accuracy (achieved overall 94% and 99%) and loss (achieved overall 0.28 and 0.009). These findings contribute to transportation IoT systems security by proposing a robust framework for enhancing the security and privacy of CAVs against cyber threats.

Keywords

• Federated learning
• deep learning
• machine learning
• transportation systems
• CAV
• IDS