Multi-attack and multi-classification intrusion detection for vehicle-mounted networks based on mosaic-coded convolutional neural network.

With the development of Internet of vehicles, the information exchange between vehicles and the outside world results in a higher risk of external network attacks to the vehicles. The attack modes to the most widely used vehicle-mounted CAN bus are complex and diverse, but most of the intrusion detection approaches proposed by now can only detect one type of attack at a time. Aiming at detecting multi-types of attacks using a single model, we proposed a detection method based on the Mosaic-coded convolution neural network for intrusions containing various combinations of attacks with multi-classification capability. A Mosaic-like two-dimensional data grid was created from the one-dimensional CAN ID for the CNN to effectively extract the data features and maintain the time connections between the CAN IDs. Four types of attacks and all possible combinations of them were used to train and test our model. The autoencoder was also used to reduce the dimensionality of the data so as to cut down the model's complexity. Experimental results showed that the proposed method was effective in detecting all types of attack combinations with high and stable multi-classification ability.