Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management. Considering the unique characteristics of edge networks, such as limited resources, complex network faults, and the need for high real-time performance, enhancing and optimizing existing network fault diagnosis methods is necessary. Therefore, this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network (LSNN). Firstly, we use the Izhikevich neurons model to replace the Leaky Integrate and Fire (LIF) neurons model in the LSNN model. Izhikevich neurons inherit the simplicity of LIF neurons but also possess richer behavioral characteristics and flexibility to handle diverse data inputs. Inspired by Fast Spiking Interneurons (FSIs) with a high-frequency firing pattern, we use the parameters of FSIs. Secondly, inspired by the connection mode based on spiking dynamics in the basal ganglia (BG) area of the brain, we propose the pruning approach based on the FSIs of the BG in LSNN to improve computational efficiency and reduce the demand for computing resources and energy consumption. Furthermore, we propose a multiple iterative Dynamic Spike Timing Dependent Plasticity (DSTDP) algorithm to enhance the accuracy of the LSNN model. Experiments on two server fault datasets demonstrate significant precision, recall, and F1 improvements across three diagnosis dimensions. Simultaneously, lightweight indicators such as Params and FLOPs significantly reduced, showcasing the LSNN’s advanced performance and model efficiency. To conclude, experiment results on a pair of datasets indicate that the LSNN model surpasses traditional models and achieves cutting-edge outcomes in network fault diagnosis tasks.

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.