Work

Development of an In-Vehicle Intrusion Detection Model Integrating Federated Learning and LSTM Networks

Year: 2025

Type: article

Abstract: Introduction: Ensuring vehicular cybersecurity is a critical challenge due to the increasing connectivity of modern vehicles, and traditional centralised learning approaches for intrusion detection pose significant privacy risks, as they require sensitive data to be shared from multiple vehicles to a central server. Objective: The aim of this study is therefore to develop an in-vehicle intrusion detection system (IVIDS) that integrates federated learning (FL) with neural networks, enabling decentralised and privacy-preserving detection of cyberattacks in vehicular networks. The proposed system extends previous research by detecting a broader range of attacks (eight types) and exploring different deep learning architectures. Methods: This study employs an extended version of the publicly available VeReMi dataset to train and evaluate multiple neural network architectures, including Multilayer Perceptrons (MLPs), Gated Recurrent Units (GRUs), and Long Short-Term Memory (LSTM) networks. Federated learning is utilised to enable collaborative model training across multiple vehicles without sharing raw data. Various data preprocessing techniques and differential privacy mechanisms are also explored. Results and Conclusions: The experimental results demonstrate that LSTM networks outperform both MLP and GRU architectures in classifying vehicular cyberattacks. The best LSTM model, trained with two previous message lags and standard normalisation, achieved a classification accuracy of 96.75% in detecting eight types of attacks, surpassing previous studies, and demonstrating the potential of applying neural networks designed to work with time series data.

Source: Information

DOI: 10.3390/info16040292

Authors: Miriam Zambudio Martínez, Rafael Marin-Perez, Antonio Fernando Skarmeta Gomez

Institution: Universidad de Murcia

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,930