The invention discloses an Internet of Vehicles
CAN bus intrusion detection method based on
noise perception active learning, and belongs to the technical field of Internet of Vehicles safety and
machine learning. The invention aims to solve the technical problems of false
label noise interference, high manual labeling cost, high
attack missing report rate caused by
class imbalance and the like. The core of the method is to execute a
noise sensing mixed query strategy in an iterative loop: firstly, generating a pseudo tag through clustering and correcting by using an integrated noise
detector; secondly, calculating uncertainty scores and noise probabilities of the samples, fusing the uncertainty scores and the noise probabilities to obtain a comprehensive
score, and preferentially selecting the samples with high uncertainty and
low noise probabilities; and then adaptively selecting a sampling strategy according to the model performance and applying category balance constraint. The query batch is used to iteratively update the model while dynamically adjusting the classification threshold to reduce the missing report rate. According to the method, the influence of pseudo
label noise can be effectively suppressed, and the
attack detection precision and generalization capability are remarkably improved with extremely low labeling cost.