Detecting In-vehicle Intrusion via Semi-supervised Learning-based Convolutional Adversarial Autoencoders

TL;DR

This paper introduces a semi-supervised convolutional adversarial autoencoder for detecting in-vehicle CAN bus intrusions, effectively identifying various attacks with limited labeled data and real-time performance.

Contribution

It presents a novel semi-supervised deep learning model combining autoencoders and GANs for intrusion detection in vehicle networks, reducing labeled data needs and improving efficiency.

Findings

Achieved F1 score of 0.99 in attack detection

Reduced model parameters by five times

Lowered inference time by eight times

Abstract

With the development of autonomous vehicle technology, the controller area network (CAN) bus has become the de facto standard for an in-vehicle communication system because of its simplicity and efficiency. However, without any encryption and authentication mechanisms, the in-vehicle network using the CAN protocol is susceptible to a wide range of attacks. Many studies, which are mostly based on machine learning, have proposed installing an intrusion detection system (IDS) for anomaly detection in the CAN bus system. Although machine learning methods have many advantages for IDS, previous models usually require a large amount of labeled data, which results in high time and labor costs. To handle this problem, we propose a novel semi-supervised learning-based convolutional adversarial autoencoder model in this paper. The proposed model combines two popular deep learning models: autoencoder and generative adversarial networks. First, the model is trained with unlabeled data to learn the manifolds of normal and attack patterns. Then, only a small number of labeled samples are used in supervised training. The proposed model can detect various kinds of message injection attacks, such as DoS, fuzzy, and spoofing, as well as unknown attacks. The experimental results show that the proposed model achieves the highest F1 score of 0.99 and a low error rate of 0.1\% with limited labeled data compared to other supervised methods. In addition, we show that the model can meet the real-time requirement by analyzing the model complexity in terms of the number of trainable parameters and inference time. This study successfully reduced the number of model parameters by five times and the inference time by eight times, compared to a state-of-the-art model.