The complexity of alarm detection and diagnosis tasks often results in a lack of alarm log data. Due to the strong rule associations inherent in alarm log data, existing data augmentation algorithms cannot obtain good results for alarm log data. To address this problem, this paper introduces a new algorithm for augmenting alarm log data, termed APRGAN, which combines a generative adversarial network (GAN) with the Apriori algorithm. APRGAN generates alarm log data under the guidance of rules mined by the rule miner. Moreover, we propose a new dynamic updating mechanism to alleviate the mode collapse problem of the GAN. In addition to updating the real reference dataset used to train the discriminator in the GAN, we dynamically update the parameters and the rule set of the Apriori algorithm according to the data generated in each epoch. Through extensive experimentation on two public datasets, it is demonstrated that APRGAN surpasses other data augmentation algorithms in the domain with respect to alarm log data augmentation, as evidenced by its superior performance on metrics such as BLEU, ROUGE, and METEOR.