FL-MAAE: An Intrusion Detection Method for the Internet of Vehicles Based on Federated Learning and Memory-Augmented Autoencoder

Abstract

The Internet of Vehicles (IoV) is a network system that enables wireless communication and information exchange between vehicles and other traffic participants. Intrusion detection plays a very important role in the IoV. However, with the development of the IoV, unknown attack behaviors may appear. The lack of analysis and collection of these attack behavior has led to an imbalance in the sample data categories of the IoV intrusion detection, which causes the problem of low detection accuracy. At the same time, the intrusion detection model usually needs to upload data to the cloud for training, which will introduce the privacy risk due to of the leakage of vehicle users’ information. In this paper, we propose an intrusion detection method for the IoV based on federated learning and memory-augmented autoencoder (FL-MAAE). We add a memory module to the autoencoder model to enhance its ability to store the behavior feature patterns of the IoV, make it robust to imbalanced samples, and use the reconstruction error as the evaluation index, so as to detect unknown attacks in the IoV. We propose a federated learning based training method for the IoV intrusion detection model. Local training of intrusion detection models in roadside units can effectively protect the privacy of data resources. We also designed an aggregation method based on the performance contribution of participants to improve the reliability of model aggregation. We conducted experiments on the NSL-KDD intrusion detection dataset to evaluate the performance of the proposed method. Experimental results show that our method has the best intrusion detection performance. In the case of contaminated samples, the accuracy and F1 score of the proposed method are 9.6% and 7.39% higher than those of the comparison methods on average.