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Regular Paper

Alarm Log Data Augmentation Algorithm Based on a GAN Model and Apriori

State Key Laboratory of Network and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China
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Abstract

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.

Keywords

data augmentationalarm log dataApriorigenerative adversarial network (GAN)

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Journal of Computer Science and Technology
Volume 39 Issue 4,
August 2024
Pages 951-966
DOI: 10.1007/s11390-024-2408-1
Cite this article:
Yang Y, Li Y-T, Huo Y-H, et al. Alarm Log Data Augmentation Algorithm Based on a GAN Model and Apriori. Journal of Computer Science and Technology, 2024, 39(4): 951-966. https://doi.org/10.1007/s11390-024-2408-1

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Received: 13 April 2022
Accepted: 12 June 2024
Published: 20 September 2024
© Institute of Computing Technology, Chinese Academy of Sciences 2024
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