Federated AI-Enabled In-Vehicle Network Intrusion Detection for Internet of Vehicles

Abstract

The integration of artificial intelligence (AI) technology into the Internet of Vehicles (IoV) has provided smart services for intelligent connected vehicles (ICVs). However, due to gradually upgrading to ICVs, an increasing number of external communications interfaces exposes the in-vehicle networks (IVNs) to malicious network intrusion. The malicious intruders can take over the compromised ICVs and mediately intrude into the ICVs connected through IoV. Therefore, it is urgent to develop IVN intrusion detection methods for IoV security protection. In this paper, a ConvLSTM-based IVN intrusion detection method is developed by leveraging the periodicity of the network message ID. For training the ConvLSTM model, a federated learning (FL) framework with client selection is proposed. The fundamental FL framework works in the client-server mode. ICVs are the local clients, and mobile edge computing (MEC) servers connected to base stations (BSs) function as the parameter servers. Based on the framework, a proximal policy optimization (PPO)-based federated client selection (FCS) scheme is further developed to optimize the model accuracy and system overhead of federated ConvLSTM model training. Simulations are conducted by the exploitation of real-world IoV scenario settings and IVN datasets. The results indicate that by exploiting the ConvLSTM, the model size and convergence time are dramatically reduced, and the 95%-beyond detection accuracy is maintained. The results also unveil that the PPO-based FCS scheme outperforms the benchmarks on the convergence rate, model accuracy, and system overhead.