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 0.9984 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.

随着自动驾驶汽车技术的发展，控制器局域网 (CAN) 总线因其简单性和高效性已成为车载通信系统的事实标准。然而，在没有任何加密和认证机制的情况下，使用 CAN 协议的车载网络容易受到广泛的攻击。许多主要基于机器学习的研究建议在 CAN 总线系统中安装入侵检测系统 (IDS) 以进行异常检测。虽然机器学习方法对于 IDS 有很多优势，但以前的模型通常需要大量的标记数据，这导致了高昂的时间和人力成本。为了解决这个问题，我们在本文中提出了一种新颖的基于半监督学习的卷积对抗自动编码器模型。所提出的模型结合了两种流行的深度学习模型：自动编码器和生成对抗网络。首先，使用未标记数据对模型进行训练，以学习正常模式和攻击模式的流形。然后，在监督训练中只使用少量标记样本。所提出的模型可以检测各种类型的消息注入攻击，如 DoS、模糊和欺骗，以及未知攻击。实验结果表明，与其他监督方法相比，所提出的模型在标记数据有限的情况下实现了最高的 F1 分数 0.9984 和 0.1% 的低错误率。此外，我们通过分析模型在可训练参数数量和推理时间方面的复杂性，表明该模型可以满足实时性要求。