Intrusion Detection for In-vehicle Network by Using Single GAN in Connected Vehicles

Controller area network (CAN) bus-based connected and even self-driving vehicles suffer severe cybersecurity challenges because connections from outside the vehicle and an existing security vulnerability on CAN expose passengers to privacy and security threats. Generative adversarial nets (GAN)-based intrusion detection systems (IDSs) for in-vehicle network can eliminate the limit of insufficient types of attack data suffered by the deep learning-based IDSs. The existing GAN-based IDS is a hybrid deep learning model built by DNN and GAN, which is too complex to have a short detection time. The evaluation performance of this model can be further improved. To mitigate this issue, we propose another GAN-based intrusion detection method for in-vehicle network, which is a single improved GAN. The proposed model can have better evaluation metrics, e.g., the testing accuracy rate is up to 99.8% and poor detection performance is addressed when a single GAN is used in intrusion detection for the in-vehicle network. In this paper, we design a new loss function for generator in GAN to enhance an ability to produce fake abnormal data, and utilize a sparse enhancement training method helping discriminator in GAN to correct the arbitration bias for fake attack data every 100 steps. In addition, we utilize fewer convolution and de-convolution layers for constructing GAN model, which can reduce the calculation time theoretically and ultimately shorten the detection time to 0.12 +/- 0.03 width=".17em"ms for a data block built by 64 CAN messages. We evaluate this improved GAN-based intrusion detection by test set. The results demonstrate that our method has not only a capacity of five classifications, but also better evaluation performance than the existing method in the area of GAN-based IDSs for the in-vehicle network.