Detecting In-vehicle Intrusion via Semi-supervised Learning-based Convolutional Adversarial Autoencoders. (arXiv:2204.01193v1 [cs.CR])

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.